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Research Article

The link‐prediction problem for social networks

David Liben‐Nowell

E-mail address:dlibenno@carleton.edu

Department of Computer Science, Carleton College, Northfield, MN 55057

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Jon Kleinberg

E-mail address:kleinber@cs.cornell.edu

Department of Computer Science, Cornell University, Ithaca, NY 14853

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First published: 26 March 2007
Cited by: 980
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Abstract

Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link‐prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.

Introduction

As part of the recent surge of research on large, complex networks and their properties, a considerable amount of attention has been devoted to the computational analysis of social networks—structures whose nodes represent people or other entities embedded in a social context, and whose edges represent interaction, collaboration, or influence between entities. Natural examples of social networks include the set of all scientists in a particular discipline, with edges joining pairs who have coauthored articles; the set of all employees in a large company, with edges joining pairs working on a common project; or a collection of business leaders, with edges joining pairs who have served together on a corporate board of directors. The increased availability of large, detailed datasets encoding such networks has stimulated extensive study of their basic properties, and the identification of recurring structural features (e.g., see the work of Adamic & Adar, 2003; Grossman, 2002; Newman, 2002; Watts, 1999; Watts & Strogatz, 1998; for a thorough recent survey, see Newman, 2003).

Social networks are highly dynamic objects; they grow and change quickly over time through the addition of new edges, signifying the appearance of new interactions in the underlying social structure. Identifying the mechanisms by which they evolve is a fundamental question that is still not well understood, and it forms the motivation for our work here. We define and study a basic computational problem underlying social‐network evolution, the link‐prediction problem: Given a snapshot of a social network at time t, we seek to accurately predict the edges that will be added to the network during the interval from time t to a given future time t9.

In effect, the link‐prediction problem asks: To what extent can the evolution of a social network be modeled using features intrinsic to the network itself? Consider a coauthorship network among scientists, for example. There are many reasons exogenous to the network why two scientists who have never written an article together will do so in the next few years: For example, they may happen to become geographically close when one of them changes institutions. Such collaborations can be hard to predict. But one also senses that a large number of new collaborations are hinted at by the topology of the network: Two scientists who are “close” in the network will have colleagues in common and will travel in similar circles; this social proximity suggests that they themselves are more likely to collaborate in the near future. Our goal is to make this intuitive notion precise and to understand which measures of “proximity” in a network lead to the most accurate link predictions. We find that a number of proximity measures lead to predictions that outperform chance by factors of 40 to 50, indicating that the network topology does indeed contain latent information from which to infer future interactions. Moreover, certain fairly subtle measures—involving infinite sums over paths in the network—often outperform more direct measures such as shortest‐path distances and numbers of shared neighbors.

We believe that a primary contribution of the present article is in the area of network‐evolution models. While there has been a proliferation of such models in recent years—e.g., see the work of Barabasi et al., 2002; Davidsen, Ebel, and Bornholdt, 2002; Jin, Girvan, and Newman, 2001; for recent work on collaboration networks, or the survey of Newman, 2003—these models have generally been evaluated only by asking whether they reproduce certain global structural features observed in real networks. As a result, it has been difficult to evaluate and compare different approaches on a principled footing. Link prediction, on the other hand, offers a very natural basis for such evaluations: A network model is useful to the extent that it can support meaningful inferences from observed network data. One sees a related approach in recent work of Newman (2001a), who considers the correlation between certain network‐growth models and data on the appearance of edges of coauthorship networks.

In addition to its role as a basic question in social‐network evolution, the link‐prediction problem could be relevant to a number of interesting current applications of social networks. Increasingly, for example, researchers in artificial intelligence and data mining have argued that a large organization (e.g., a company) can benefit from the interactions within the informal social network among its members; these ties serve to supplement the official hierarchy imposed by the organization itself (Kautz, Selman, & Shah, 1997; Raghavan, 2002). Effective methods for link prediction could be used to analyze such a social network to suggest promising interactions or collaborations that have not yet been identified within the organization. In a different vein, research in security has recently begun to emphasize the role of social‐network analysis, largely motivated by the problem of monitoring terrorist networks; link prediction in this context allows one to conjecture that particular individuals are working together even though their interaction has not been directly observed (Krebs, 2002).

The link‐prediction problem also is related to the problem of inferring missing links from an observed network: In a number of domains, one constructs a network of interactions based on observable data and then tries to infer additional links that, while not directly visible, are likely to exist (Goldberg & Roth, 2003; Popescul & Ungar, 2003; Taskar, Wong, Abbeel, & Koller, 2003). This line of work differs from our problem formulation in that it works with a static snapshot of a network rather than considering network evolution; it also tends to take into account specific attributes of the nodes in the network rather than evaluating the power of prediction methods that are based purely on the graph structure.

We turn to a description of our experimental setup in the next section. Our primary focus is on understanding the relative effectiveness of network‐proximity measures adapted from techniques in graph theory, computer science, and the social sciences, and we review a large number of such techniques. Finally, we discuss the results of our experiments.

Data and Experimental Setup

Suppose that we have a social network G = 〈V, E〉 in which each edge e = 〈u, v〉 ∈ E represents an interaction between u and v that took place at a particular time t(e). We record multiple interactions between u and v as parallel edges, with potentially different timestamps. For two times t < t′, let G[t, t′] denote the subgraph of G consisting of all edges with a timestamp between t and t′. Here, then, is a concrete formulation of the link‐prediction problem. We choose four times t0 < t0′ < t1 < t1′ and give an algorithm access to the network G[t0, t0′]; it must then output a list of edges not present in G[t0, t0′] that are predicted to appear in the network G[t1, t1′]. We refer to [t0, t0′] as the training interval and [t1, t1′] as the test interval.

Of course, social networks grow through the addition of nodes as well as edges, and it is not sensible to seek predictions for edges whose endpoints are not present in the training interval. Thus, in evaluating link‐prediction methods, we will generally use two parameters, κtraining and κtest, and define the set Core to consist of all nodes that are incident to at least κtraining edges in G[t0, t0′] and at least κtest edges in G[t1, t1′]. We will then evaluate how accurately the new edges between elements of Core can be predicted.

We now describe our experimental setup more specifically. We work with five coauthorship networks G, obtained from the author lists of articles contained in five sections of the physics e‐Print arXiv, www.arxiv.org (see Figure 1 for statistics on the sizes of each of these five networks). Some heuristics were used to deal with occasional syntactic anomalies, and authors were identified by first initial and last name, a process that introduces a small amount of noise due to multiple authors with the same identifier (Newman, 2001b). The errors introduced by this process appear to be minor.

The five sections of the arXiv from which coauthorship networks were constructed: astro‐ph (astrophysics), cond‐mat (condensed matter), gr‐qc (general relativity and quantum cosmology), hep‐ph (high energy physics–phenomenology), and hep‐th (high energy physics–theory). The set Core is the subset of the authors who have written at least κtraining = 3 articles during the training period 1994–1996 and κtest = 3 articles during the test period 1997–1999. The sets Eold and Enew denote undirected edges between Core authors that first appear during the training and test periods, respectively.

Now consider any one of these five graphs. We define the training interval to be the 3 years from 1994 through 1996, and the test interval to be the 3 years from 1997 through 1999. We denote the subgraph G[1994, 1996] on the training interval by Gcollab: = 〈A, Eold〉 and use Enew to denote the set of edges 〈u, v〉 such that u, vA, and u, v coauthor an article during the test interval, but not the training interval—these are the new interactions we are seeking to predict. In our experiments on the arXiv, we can identify which authors are active throughout the entire period on the basis of the number of articles published and not on the number of coauthors. Thus, here we define the set Core to consist of all authors who have written at least κtraining := 3 articles during the training period and at least κtest :=3 articles during the test period.

Evaluating a Link Predictor

Each link predictor p that we consider outputs a ranked list Lp of pairs in A × AEold; these are predicted new collaborations, in decreasing order of confidence. For our evaluation, we focus on the set Core, so we define Enew* := Enew ∩ (Core × Core) and n := |Enew*|. Our performance measure for Predictor p is then determined as follows: From the ranked list Lp, we take the first n pairs that are in Core × Core, and determine the size of the intersection of this set of pairs with the set Enew*.

Methods for Link Prediction

In this section, we survey an array of methods for link prediction. All the methods assign a connection weight score(x, y) to pairs of nodes 〈x, y〉, based on the input graph Gcollab, and then produce a ranked list in decreasing order of score(x, y). Thus, they can be viewed as computing a measure of proximity or “similarity” between nodes x and y, relative to the network topology. In general, the methods are adapted from techniques used in graph theory and in social‐network analysis; in a number of cases, these techniques were not designed to measure node‐to‐node similarity and hence need to be modified for this purpose. Figure 2 summarizes most of these measures; we discuss them in more detail later. Note that some of these measures are designed only for connected graphs; because each graph Gcollab that we consider has a giant component—a single component containing most of the nodes—it is natural to restrict the predictions for these measures to this component.

Values for score(x, y) under various predictors; each predicts pairs 〈x, y〉 in descending order of score(x, y). The set Γ(x) consists of the neighbors of the node x in Gcollab.

Perhaps the most basic approach is to rank pairs x, y by the length of their shortest path in Gcollab. Such a measure follows the notion that collaboration networks are “small worlds,” in which individuals are related through short chains (Newman, 2001b) (In keeping with the notion that we rank pairs in decreasing order of score(x,y), we define score(x,y) here to be the negative of the shortest path length.) Pairs with shortest‐path distance equal to one are joined by an edge in Gcollab, and hence they belong to the training edge set Eold. For all of our graphs Gcollab, there are well more than n pairs at shortest‐path distance two, so our shortest‐path predictor simply selects a random subset of these distance‐two pairs.

Methods Based on Node Neighborhoods

For a node x, let Γ(x) denote the set of neighbors of x in Gcollab. A number of approaches are based on the idea that two nodes x and y are more likely to form a link in the future if their sets of neighbors Γ(x) and Γ(y) have large overlap; this approach follows the natural intuition that such nodes x and y represent authors who have many colleagues in common and hence who are more likely to come into contact themselves. Jin et al. (2001) and Davidsen et al. (2002) defined abstract models for network growth using this principle, in which an edge 〈x, y〉 is more likely to form if edges 〈x, z〉 and 〈z, y〉 are already present for some z.

Common neighbors.

The most direct implementation of this idea for link prediction is to define score(x, y) := | Γ(x∩) ?Γ(y)|, the number of neighbors that x and y have in common. Newman (2001a) computed this quantity in the context of collaboration networks, verifying a correlation between the number of common neighbors of x and y at time t and the probability that they will collaborate in the future.

Jaccard's coefficient and Adamic/Adar.

The Jaccard coefficient—a commonly used similarity metric in information retrieval (Salton & McGill, 1983)—measures the probability that both x and y have a feature f, for a randomly selected feature f that either x or y has. If we take “features” here to be neighbors in Gcollab, this approach leads to the measure score(x,y) := |Γ(x) ∩ Γy) |/|Γ(x) ∪ Γ(y) |.

Adamic and Adar (2003) considered a similar measure, in the context of deciding when two personal home pages are strongly “related.” To do this, they computed features of the pages and defined the similarity between two pages to be

This quantity refines the simply counting of common features by weighting rarer features more heavily. This idea suggests the measure score(x, y)$: = \sum\nolimits_{z \in \Gamma (x) \cap \Gamma (y)} {{\textstyle{1 \over {\log |\Gamma (z)|}}}}$equation image

Preferential attachment.

This has received considerable attention as a model of the growth of networks (Barabási & Albert, 1999; Mitzenmacher, 2004). The basic premise is that the probability that a new edge has node x as an endpoint is proportional to |Γ(x)|, the current number of neighbors of x. Newman (2001a) and Barabási et al. (2002) further proposed, on the basis of empirical evidence, that the probability of coauthorship of x and y is correlated with the product of the number of collaborators of x and y. This proposal corresponds to the measure score(x, y) |Γ(x)| · |Γ(y)|.

Methods Based on the Ensemble of All Paths

A number of methods refine the notion of shortest‐path distance by implicitly considering the ensemble of all paths between two nodes.

Katz (1953).

Katz defined a measure that directly sums over this collection of paths, exponentially damped by length to count short paths more heavily. This notion leads to the measure

where pathsx, y〈𝓁〉 is the set of all length‐𝓁 paths from x to y, and β >. 0 is a parameter of the predictor (A very small β yields predictions much like common neighbors because paths of length 3 or more contribute very little to the summation.) One can verify that the matrix of scores is given by (I − βM)−1I, where M is the adjacency matrix of the graph. We consider two variants of this Katz measure: (a) unweighted, in which pathsx, y〈1〉 = 1 if x and y have collaborated and 0 otherwise, and (b) weighted, in which pathsx, y〈1〉 is the number of times that x and y have collaborated.

Hitting time, PageRank, and variants.

A random walk on Gcollab starts at a node x and iteratively moves to a neighbor of x chosen uniformly at random from the set Γ(x). The hitting time Hx,y from x to y is the expected number of steps required for a random walk starting at x to reach y. Because the hitting time is not in general symmetric, it also is natural to consider the commute time Cx,y:= Hx,y + Hy,x. Both of these measures serve as natural proximity measures and hence (negated) can be used as score(x, y).

One difficulty with hitting time as a measure of proximity is that Hx,y is quite small whenever y is a node with a large stationary probability πy, regardless of the identity of x. To counterbalance this phenomenon, we also consider normalized versions of the hitting and commute times, by defining score(x, y) := −Hy,x · πy or score(x, y) := −(Hy,x · πy + );Hy,x ċ πx).

Another difficulty with these measures is their sensitive dependence to parts of the graph far away from x and y, even when x and y are connected by very short paths. A way of counteracting this dependence is to allow the random walk from x to y to periodically “reset,” returning to x with a fixed probability α at each step; in this way, distant parts of the graph almost never will be explored. Random resets form the basis of the PageRank measure for Web pages (Brin & Page, 1998), and we can adapt it for link prediction as follows. Define score(x, y) under the rooted PageRank measure with parameter α ∈ [0, 1] to be the stationary probability of y in a random walk that returns to x with probability α each step, moving to a random neighbor with probability 1 − α. Similar approaches have been considered for personalized PageRank, in which one wishes to rank Web pages based both on their overall importance, the core of PageRank, and their relevance to a particular topic or individual, by biasing the random resets toward topically relevant or bookmarked pages (Haveliwala, 2003; Haveliwala, Kamvar, & Jeh, 2003; Jeh & Widom, 2003; Kamvar, Haveliwala, Manning, & Golub, 2003).

SimRank (Jeh & Widom, 2002).

SimRank is a fixed point of the following recursive definition: Two nodes are similar to the extent that they are joined to similar neighbors. Numerically, this quantity is specified by defining similarity(x, x)1 and

for a parameter γ ∈ [0, 1]. We then define score(x, y) := similarity(x, y). SimRank also can be interpreted in terms of a random walk on the collaboration graph: It is the expected value of γ𝓁, where 𝓁 is a random variable giving the time at which random walks started from x and y first meet.

Higher Level Approaches

We now discuss three “meta‐approaches” that can be used in conjunction with any of the methods discussed earlier.

Low‐rank approximation.

Because the adjacency matrix M can be used to represent the graph Gcollab, all of our link‐prediction methods have an equivalent formulation in terms of this matrix M. In some cases, this correspondence was noted explicitly earlier (e.g., in the case of the Katz similarity score), but in many other cases the matrix formulation also is quite natural. For example, the common‐neighbors method consists simply of mapping each node x to its row r(x) in M, and then defining score(x, y) to be the inner product of the rows r(x) and r(y).

A common general technique when analyzing the structure of a large matrix M is to choose a relatively small number k and compute the rank‐k matrix Mk that best approximates M with respect to any of a number of standard matrix norms. This computation can be done efficiently using the singular‐value decomposition, and it forms the core of methods such as latent semantic analysis in information retrieval (Deerwester, Dumais, Furnas, Landauer, & Harshman, 1990). Intuitively, working with Mk rather than M can be viewed as a type of “noise‐reduction” technique that generates most of the structure in the matrix, but with a greatly simplified representation.

In our experiments, we investigate three applications of low‐rank approximation: (a) ranking by the Katz measure, in which we use Mk rather than M in the underlying formula; (b) ranking by common neighbors, in which we score by inner products of rows in Mk rather than M; and—most simply of all—(c) defining score(x, y) to be the 〈x, y〉 entry in the matrix Mk.

Unseen bigrams.

Link prediction is akin to the problem of estimating frequencies for unseen bigrams in language modeling—pairs of words that co‐occur in a test corpus, but not in the corresponding training corpus (e.g., see the work of Essen & Steinbiss, 1992). Following ideas proposed in that literature (e.g., Lee, 1999), we can augment our estimates for score(x, y) using values of score(z, y) for nodes z that are “similar” to x. Specifically, we adapt this approach to the link‐prediction problem as follows. Suppose we have values score(x,y) computed under one of the measures above. Let Sxδ) denote the δ nodes most related to x under score(x,·), for a parameter δ ∈ ℤ+. We then define enhanced scores in terms of the nodes in this set:

Clustering.

One might seek to improve on the quality of a predictor by deleting the more “tenuous” edges in Gcollab through a clustering procedure, and then running the predictor on the resulting “cleaned‐up” subgraph. Consider a measure computing values for score(x, y). We compute score(u, v) for all edges in Eold, and delete the (1−ρpar; fraction of these edges for which the score is lowest, for a parameter ρ ∈ [0, 1]. We now recompute score(x, y) for all pairs 〈x, y〉 on this subgraph; in this way, we determine node proximities using only edges for which the proximity measure itself has the most confidence.

Results and Discussion

As discussed in the first section, many collaborations form (or fail to form) for reasons outside the scope of the network; thus, the raw performance of our predictors is relatively low. To more meaningfully represent predictor quality, we use as our baseline a random predictor, which simply predicts randomly selected pairs of authors who did not collaborate in the training interval. The probability that a random prediction is correct is just the ratio between |Enew|, the number of possible correct predictions, and $\left( \matrix{ |{\rm{Core|}} \cr {\rm{2}} \cr} \right) - |E_{old} |,$equation image, the number of possible predictions that can be made (Any pair chosen from the set Core of core authors is a legal prediction unless they had already collaborated, which occurs for | Eold | pairs.) A random prediction is correct with probability between 0.15% (cond‐mat) and 0.48% (astro‐ph).

Figures 3 and 4 show each predictor's performance on each arXiv section, in terms of the factor improvement over random. One can use standard tail inequalities (e.g., see the text of Motwani & Raghavan, 1995) to show that the probability of a random predictor's performance exceeding its expectation by a factor of 5 is very small: this probability ranges from about 0.004 for gr‐qc to about 10−48 for astro‐ph. Thus, almost every predictor performs significantly better than random predictions on every dataset.

Performance of various predictors on the link‐prediction task defined in the section on Data and Experimental Setup. For each predictor and each arXiv section, the given number specifies the factor improvement over random prediction. Two predictors in particular are used as baselines for comparison: graph distance and common neighbors (see Methods for definitions). Italicized entries have performance at least as good as the graph‐distance predictor; bold entries are at least as good as the common‐neighbors predictor (see also Figure 4).

Performance of various meta‐approaches on the link‐prediction task defined in the Data and Experimental Setup section. As before, for each predictor and each arXiv section, the given number specifies the factor improvement over random predictions (see Figure 3).

Figures 5, 6, and 7 show the average relative performance of several different predictors versus three baseline predictors—the random predictor, the graph‐distance predictor, and the common‐neighbors predictor. There is no single clear winner among the techniques, but we see that a number of methods significantly outperform the random predictor, suggesting that there is indeed useful information contained in the network topology alone. The Katz measure and its variants based on clustering and low‐rank approximation perform consistently well; on three of the five arXiv sections, a variant of Katz achieves the best performance. Some of the very simple measures also perform surprisingly well, including common neighbors and the Adamic/Adar measure.

Relative average performance of various predictors versus random predictions. The value shown is the average ratio over the five datasets of the given predictor's performance versus the random predictor's performance. The error bars indicate the minimum and maximum of this ratio over the five datasets. The parameters for the starred predictors are as follows: (a) for weighted Katz, β =0.005; (b) for Katz clustering, β1 =0.001, ρ =0.15, β2 =0.1; (c) for low‐rank inner product, rank =256; (d) for rooted Pagerank, α = 0.15; (e) for unseen bigrams, unweighted common neighbors with δ = 8; and (f) for SimRank, γ = 0.8.

Relative average performance of various predictors versus the graph‐distance predictor. The plotted value shows the average taken over the five datasets of the ratio of the performance of the given predictor versus the graph‐distance predictor; the error bars indicate the range of this ratio over the five datasets. All parameter settings are as in Figure 5.

Relative average performance of various predictors versus the common‐neighbors predictor, as in Figure 6. Error bars display the range of the performance ratio of the given predictor versus common neighbors over the five datasets; the displayed value gives the average ratio. Parameter settings are as in Figure 5.

Similarities Among the Predictors and the Datasets

Not surprisingly, there is significant overlap in the predictions made by the various methods. In Figure 8, we show the number of common predictions made by 10 of the most successful measures on the cond‐mat graph. We see that Katz, low‐rank inner product, and Adamic/Adar are quite similar in their predictions, as are (to a somewhat lesser extent) rooted PageRank, SimRank, and Jaccard. Hitting time is remarkably unlike any of the other nine in its predictions, despite its reasonable performance. The number of common correct predictions shows qualitatively similar behavior (see Figure 9). It would be interesting to understand the generality of these overlap phenomena, especially because certain of the large overlaps do not seem to follow obviously from the definitions of the measures.

The number of common predictions made by various predictors on the cond‐mat dataset, of 1,150 predictions. Parameter settings are as in Figure 5.

The number of correct common predictions made by various predictors on the cond‐mat dataset, of 1,150 predictions. The diagonal entries indicate the number of correct predictions for each predictor. Parameter settings are as in Figure 5.

It is harder to quantify the differences among the datasets, but their relationship is a very interesting issue as well. One perspective is provided by the methods based on low‐rank approximation: On four of the datasets, their performance tends to be best at an intermediate rank while on gr‐qc they perform best at rank 1 (see Figure 10, e.g., for a plot of the change in the performance of the low‐rank matrix‐entry predictor as the rank of the approximation varies.) This fact suggests a sense in which the collaborations in gr‐qc have a much “simpler” structure than those in the other four. One also observes the apparent importance of node degree in the hep‐ph collaborations: The preferential‐attachment predictor—which considers only the number (and not the identity) of a scientist's coauthors—does uncharacteristically well on this dataset, outperforming the basic graph‐distance predictor. Finally, it would be interesting to make precise a sense in which astro‐ph is a “difficult” dataset, given the low performance of all methods relative to random and the fact that none beats simple ranking by common neighbors. We will explore this issue further when we consider collaboration data drawn from other fields.

Relative performance of the low‐rank matrix‐entry predictor for various ranks on each arXiv section. For each arXiv section, the performance of this predictor, measured by the factor of improvement over random predictions, is shown for ranks 1, 4, 16, 64, 256, and 1024. Notice that for all arXiv sections save gr‐qc, predictor performance is maximized by an intermediate rank; for that dataset, performance continues to improve as the rank decreases all the way to rank 1.

Because almost all of our experiments were carried out on social networks formed via the collaborations of physicists, it is difficult to draw broad conclusions about link prediction in social networks in general. The culture of physicists and of physics collaboration (e.g., see the work of Katz & Martin, 1997) plays a role in the quality of our results. The considerations discussed earlier suggest that there are some important differences even within physics (depending on the subfield), and an important area for future study is to understand how other social networks differ from the ones that we studied here.

Small Worlds

It is reassuring that even the basic graph‐distance predictor handily outperforms random predictions, but this measure has severe limitations. Extensive research has been devoted to understanding the so‐called small‐world problem in collaboration network; that is, accounting for the existence of short paths connecting virtually every pair of scientists (Newman, 2001b). This property is normally viewed as a vital fact about the scientific community (New ideas spread quickly, and every discipline interacts with—and gains from—other fields.), but in the context of our prediction task, we come to a different conclusion: The small‐world problem is really a problem. The shortest path between two scientists in wholly unrelated disciplines is often very short (and very tenuous). To take one particular, but not atypical, example, the developmental psychologist Jean Piaget has as small an Erdös number—3 (Castro & Grossman, 1999)—as most mathematicians and computer scientists. Overall, the basic graph‐distance predictor is not competitive with most of the other approaches studied; our most successful link predictors can be viewed as using measures of proximity that are robust to the few edges that result from rare collaborations between fields.

Restricting to Distance 3

The small‐world problem suggests that there are many pairs of authors separated by a graph distance of 2 who will not collaborate, but we also observe the dual problem: Many pairs who collaborate are at distance greater than 2. Between 71 (hep‐ph) and 83% (cond‐mat) of new edges form between pairs at distance 3 or greater (see Figure 11).

Relationship between new collaborations and graph distance.

Because most new collaborations are not at distance 2, we also are interested in how well our predictors perform when we disregard all distance‐2 pairs. Clearly, nodes at distances greater than 2 have no neighbors in common, and hence this task essentially rules out the use of methods based on common neighbors. The performance of the other measures is shown in Figure 12. The graph‐distance predictor (i.e., predicting all distance‐3 pairs) performs between about three and nine times random and is consistently beaten by virtually all of the predictors: SimRank, rooted PageRank, Katz, and the low‐rank and unseen‐bigram techniques. The unweighted Katz and unseen‐bigram predictors have the best performance (as high as about 30 times random, on gr‐qc), followed closely by weighted Katz, SimRank, and rooted PageRank.

The distance‐three task: performance of predictors only on edges in Enew for which the endpoints were at distance three or more in Gcollab. Methods based on common neighbors are not appropriate for this task (see Results and Discussion).

The Breadth of the Data

We also have considered three other datasets: (a) the proceedings of two conferences in theoretical computer science, Symposium on the Theory of Computing (STOC) and Foundations of Computer Science (FOCS), (b) the articles found in the Citeseer (www.citeseer.com) online database, which finds articles by crawling the Web for any files in postscript format, and (c) all five of the arXiv sections merged into one. Consider the performance of the common‐neighbors predictor compared to random on these datasets:

STOC/FOCS arXiv sections combined arXiv sections Citeseer
6.1 18.0–46.9 71.2 147.0

Performance versus random swells dramatically as the topical focus of our dataset widens. That is, when we consider a more diverse collection of scientists, it is fundamentally easier to group scientists into fields of study (and therefore outperform the random predictor, which usually will make guesses between fields). When we consider a sufficiently narrow set of researchers (e.g., STOC/FOCS), almost any author can collaborate with almost any other author, and there seems to be a strong random component to new collaborations (In extensive experiments on the STOC/FOCS data, we could not beat random guessing by a factor of more than about 7.) It is an interesting challenge to formalize the sense in which the STOC/FOCS collaborations are truly intractable to predict; that is, to what extent information about new collaborations is simply not present in the old collaboration data.

Future Directions

While the predictors that we have discussed perform reasonably well, even the best (Katz clustering on gr‐qc) is correct on only about 16% of its predictions. There is clearly much room for improvement in performance on this task, and finding ways to take better advantage of the information in the training data is an interesting open question. Another issue is to improve the efficiency of the proximity‐based methods on very large networks; fast algorithms for approximating the distribution of node‐to‐node distances may be one approach (Palmer, Gibbons, & Faloutsos, 2002).

The graph Gcollab is a lossy representation of the data; we also can consider a bipartite collaboration graph Bcollab, with a vertex for every author and article, and an edge connecting each article to each of its authors. The bipartite graph contains more information than Gcollab, so we may hope that predictors can use it to improve performance. The size of Bcollab is much larger than that of Gcollab, making experiments prohibitive, but we have tried using the SimRank and Katz predictors on smaller datasets (gr‐qc, or shorter training periods). Their performance does not seem to improve, but perhaps other predictors can fruitfully exploit the additional information in Bcollab.

Similarly, our experiments treat all training‐period collaborations equally. Perhaps one can improve performance by treating more recent collaborations as more important than older ones. One also could tune the parameters of the Katz predictor, for example, by dividing the training set into temporal segments, training β on the beginning, and then using the end of the training set to make final predictions.

One also might try to use additional information such as the titles of articles or the institutional affiliations of the authors to identify the specific research area or geographic location of each scientist, and then use areas/locations to predict collaborations. In the field of bibliometrics, for example, Katz (1994), Melin and Persson (1996), and Ding, Foo, and Chowdhury (1999), among others, have observed institutional and geographic correlations in collaboration; a natural further direction would be to attempt to use geographic location, for instance, as a component of a predictor. To some extent, such geographic information, or indeed any other relevant properties of the nodes, is latently present in the graph Gcollab—precisely because such factors already have played a role in the formation of old edges in the training set; however, direct access to such information may well confer additional predictive power, and it is an interesting open question to better understand the strength of such information in link prediction.

Finally, there has been relevant work in the machine‐learning community on estimating distribution support (Schölkopf, Platt, Shawe‐Taylor, Smola, & Williamson, 1999): Given samples from an unknown probability distribution P, we must find a “simple” set S so that Prx˜P[xS] < ϵ. We can view training‐period collaborations as samples drawn from a probability distribution on pairs of scientists; our goal is to approximate the set of pairs that have positive probability of collaborating. There also has been some potentially relevant work in machine learning on classification when the training set consists only of a relatively small set of positively labeled examples and a large set of unlabeled examples, with no labeled negative examples (Yu, Zhai, & Han, 2003). It is an open question whether these techniques can be fruitfully applied to the link‐prediction problem.

Acknowledgments

We thank Jon Herzog, Tommi Jaakkola, David Karger, Lillian Lee, Frank McSherry, Mike Schneider, Grant Wang, and Robert Wehr for helpful discussions and comments on earlier drafts of this article. We thank Paul Ginsparg for generously providing the bibliographic data from the arXiv.

An abbreviated preliminary version of this article appears in the Proceedings of the 12th Annual ACM International Conference on Information and Knowledge Management (CIKM'03), November 2003, pp. 556–559.

David Liben‐Nowell was supported in part by an NSF Graduate Research Fellowship. Jon Kleinberg was supported in part by a David and Lucile Packard Foundation Fellowship and NSF ITR Grant IIS‐0081334.

Number of times cited: 980

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  • , Chapter 6 Prediction of Venture Capital Coinvestment Based on Structural Balance Theory, Social Network Analysis, 10.1201/9781315369594-7, (137-176), (2017).
  • , Multirelational Recommendation in Heterogeneous Networks, ACM Transactions on the Web, 11, 3, (1), (2017).
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  • , Overlapping Community Detection Method for Social Networks, Digital Economy. Emerging Technologies and Business Innovation, 10.1007/978-3-319-62737-3_12, (143-151), (2017).
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  • , Link prediction in drug-target interactions network using similarity indices, BMC Bioinformatics, 18, 1, (2017).
  • , A systemic analysis of link prediction in social network, Artificial Intelligence Review, (2017).
  • , A Survey of Link Recommendation for Social Networks, ACM Transactions on Management Information Systems, 9, 1, (1), (2017).
  • , Understanding and forecasting lifecycle events in information cascades, Social Network Analysis and Mining, 7, 1, (2017).
  • 2017 4th NAFOSTED Conference on Information and Computer Science Hanoi 2017 4th NAFOSTED Conference on Information and Computer Science IEEE , (2017). 978-1-5386-3210-9 Link prediction in weighted network based on reliable routes by machine learning approach , (2017). 236 241 8108070 , 10.1109/NAFOSTED.2017.8108070 http://ieeexplore.ieee.org/document/8108070/
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  • , An Influence Propagation View of PageRank, ACM Transactions on Knowledge Discovery from Data, 10.1145/3046941, 11, 3, (1-30), (2017).
  • , A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories, ACM Transactions on Information Systems, 35, 4, (1), (2017).
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  • , An event detection method for social networks based on link prediction, Information Systems, 71, (16), (2017).
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  • , Link Prediction Using Top-k Shortest Distances, Data Analytics, 10.1007/978-3-319-60795-5_10, (101-105), (2017).
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  • , Querying Volatile and Dynamic Networks, Encyclopedia of Social Network Analysis and Mining, 10.1007/978-1-4614-7163-9_390-1, (1-8), (2017).
  • 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE) Galati 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE) IEEE , (2017). 978-1-5386-2059-5 Visualization and analysis of user behaviour patterns for multimedia content view in social networks , (2017). 1 7 8170685 , 10.1109/ISEEE.2017.8170685 http://ieeexplore.ieee.org/document/8170685/ 2017 IEEE International Conference on Web Services (ICWS) Honolulu, HI, USA 2017 IEEE International Conference on Web Services (ICWS) IEEE , (2017). 978-1-5386-0752-7 A Fine-Grained API Link Prediction Approach Supporting Mashup Recommendation , (2017). 220 228 8029765 , 10.1109/ICWS.2017.36 http://ieeexplore.ieee.org/document/8029765/
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  • , A utility-based link prediction method in social networks, European Journal of Operational Research, 260, 2, (693), (2017).
  • , An evidential link prediction method and link predictability based on Shannon entropy, Physica A: Statistical Mechanics and its Applications, 482, (699), (2017).
  • , Ranking in evolving complex networks, Physics Reports, 10.1016/j.physrep.2017.05.001, 689, (1-54), (2017).
  • , Clustering and Community Detection With Imbalanced Clusters, IEEE Transactions on Signal and Information Processing over Networks, 3, 1, (61), (2017).
  • the Symposium SAC '17 Marrakech, Morocco Proceedings of the Symposium on Applied Computing - SAC '17 Applied Computing ACM Press New York, New York, USA , (2017). 9781450344869 , 10.1145/3019612 20170919041750 http://dl.acm.org/citation.cfm?doid=3019612 Marketing campaign targeting using bridge extraction , (2017). 1045 1052 , 10.1145/3019612.3019814 20170919041801 http://dl.acm.org/citation.cfm?doid=3019612.3019814
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  • , Predicting brain network changes in Alzheimer's disease with link prediction algorithms, Molecular BioSystems, 13, 4, (725), (2017).
  • , Link Prediction in Temporal Heterogeneous Networks, Intelligence and Security Informatics, 10.1007/978-3-319-57463-9_6, (83-98), (2017).
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  • , Inferring Advisor-Student Relationships from Publication Networks Based on Approximate MaxConfidence Measure, Mathematical Problems in Engineering, 2017, (1), (2017).
  • , Signed Social Networks: A Survey, Advances in Computing and Data Sciences, 10.1007/978-981-10-5427-3_35, (326-335), (2017).
  • , Evaluation of large-scale transnational high-speed railway construction priority in the belt and road region, Transportation Research Part E: Logistics and Transportation Review, 10.1016/j.tre.2017.07.007, (2017).
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  • , From Relational Data to Graphs: Inferring Significant Links Using Generalized Hypergeometric Ensembles, Social Informatics, 10.1007/978-3-319-67256-4_11, (111-120), (2017).
  • , A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks, Advances in Knowledge Discovery and Data Mining, 10.1007/978-3-319-57454-7_58, (750-761), (2017).
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  • , Inferring Social Ties, Encyclopedia of Social Network Analysis and Mining, 10.1007/978-1-4614-7163-9_177-1, (1-9), (2017).
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  • , Analyzing Time-Decay Effects of Mediating-Objects in Creating Trust-Links, New Frontiers in Mining Complex Patterns, 10.1007/978-3-319-61461-8_7, (99-114), (2017).
  • 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) Valencia, Spain 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) IEEE , (2017). 978-1-5090-4372-9 Algorithm for prediction of negative links using sentiment analysis in social networks , (2017). 1570 1575 7986518 , 10.1109/IWCMC.2017.7986518 http://ieeexplore.ieee.org/document/7986518/
  • , The integration of weighted human gene association networks based on link prediction, BMC Systems Biology, 11, 1, (2017).
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  • , Detecting the emergence of new scientific collaboration links in Africa: A comparison of expected and realized collaboration intensities, Journal of Informetrics, 11, 3, (892), (2017).
  • , Link prediction in multiplex online social networks, Royal Society Open Science, 4, 2, (160863), (2017).
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  • , Automatic feature selection for supervised learning in link prediction applications: a comparative study, Knowledge and Information Systems, (2017).
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  • 2017 International Joint Conference on Neural Networks (IJCNN) Anchorage, AK, USA 2017 International Joint Conference on Neural Networks (IJCNN) IEEE , (2017). 978-1-5090-6182-2 Link prediction based hybrid recommendation system using user-page preference graphs , (2017). 1147 1154 7965981 , 10.1109/IJCNN.2017.7965981 http://ieeexplore.ieee.org/document/7965981/ the 2016 International Conference SIGMOD '16 San Francisco, California, USA Proceedings of the 2016 International Conference on Management of Data - SIGMOD '16 Management of Data ACM Press New York, New York, USA , (2016). 9781450335317 , 10.1145/2882903 20160615113224 http://dl.acm.org/citation.cfm?doid=2882903 SLING A Near-Optimal Index Structure for SimRank , (2016). 1859 1874 , 10.1145/2882903.2915243 20160615113229 http://dl.acm.org/citation.cfm?doid=2882903.2915243
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  • , Cognitive social network analysis for supporting the reliable decision-making process, The Journal of Supercomputing, (2016).
  • 2016 IEEE International Conference on Healthcare Informatics (ICHI) Chicago, IL, USA 2016 IEEE International Conference on Healthcare Informatics (ICHI) IEEE , (2016). 978-1-5090-6117-4 Discovering Drug-Drug Interactions and Associated Adverse Drug Reactions with Triad Prediction in Heterogeneous Healthcare Networks , (2016). 244 254 7776350 , 10.1109/ICHI.2016.34 http://ieeexplore.ieee.org/document/7776350/
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  • , A Survey of Signed Network Mining in Social Media, ACM Computing Surveys, 49, 3, (1), (2016).
  • 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) Chicago, IL, USA 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) IEEE , (2016). 978-1-5090-3682-0 Jaccard Coefficients as a Potential Graph Benchmark , (2016). 921 928 7529958 , 10.1109/IPDPSW.2016.208 http://ieeexplore.ieee.org/document/7529958/
  • , Exploring Hierarchies in Online Social Networks, IEEE Transactions on Knowledge and Data Engineering, 28, 8, (2086), (2016).
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  • , A User Adaptive Model for Followee Recommendation on Twitter, Natural Language Understanding and Intelligent Applications, 10.1007/978-3-319-50496-4_35, (425-436), (2016).
  • , Increasing sampling efficiency for the fixed degree sequence model with phase transitions, Social Network Analysis and Mining, 6, 1, (2016).
  • , Discriminative Link Prediction using Local, Community, and Global Signals, IEEE Transactions on Knowledge and Data Engineering, 28, 8, (2057), (2016).
  • the 7th International Conference ICCCNT '16 Dallas, TX, USA Proceedings of the 7th International Conference on Computing Communication and Networking Technologies - ICCCNT '16 Computing Communication and Networking Technologies ACM Press New York, New York, USA , (2016). 9781450341790 , 10.1145/2967878 20160912091657 http://dl.acm.org/citation.cfm?doid=2967878 Algorithm for Prediction of Links using Sentiment Analysis in Social Networks , (2016). 1 6 , 10.1145/2967878.2967911 20160912091658 http://dl.acm.org/citation.cfm?doid=2967878.2967911
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  • , Citation count prediction as a link prediction problem, Applied Intelligence, 44, 2, (252), (2016).
  • the 25th ACM International CIKM '16 Indianapolis, Indiana, USA Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16 Conference on Information and Knowledge Management ACM Press New York, New York, USA , (2016). 9781450340731 , 10.1145/2983323 20161109032246 http://dl.acm.org/citation.cfm?doid=2983323 Exploiting Cluster-based Meta Paths for Link Prediction in Signed Networks , (2016). 1905 1908 , 10.1145/2983323.2983870 20161109032255 http://dl.acm.org/citation.cfm?doid=2983323.2983870
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  • , Causality reasoning about network events for detecting stealthy malware activities, Computers & Security, 58, (180), (2016).
  • , Estimating potential trade links in the international crude oil trade: A link prediction approach, Energy, 102, (406), (2016).
  • 2016 IEEE 16th International Conference on Data Mining (ICDM) Barcelona, Spain 2016 IEEE 16th International Conference on Data Mining (ICDM) IEEE , (2016). 978-1-5090-5473-2 On Dense Subgraphs in Signed Network Streams , (2016). 51 60 7837829 , 10.1109/ICDM.2016.0016 http://ieeexplore.ieee.org/document/7837829/
  • , A Holistic Approach for Link Prediction in Multiplex Networks, Social Informatics, 10.1007/978-3-319-47874-6_5, (55-70), (2016).
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  • , When a Friend Online is More Than a Friend in Life: Intimate Relationship Prediction in Microblogs, Web Technologies and Applications, 10.1007/978-3-319-45814-4_16, (196-207), (2016).
  • , Playing the role of weak clique property in link prediction: A friend recommendation model, Scientific Reports, 6, 1, (2016).
  • , From the grid to the smart grid, topologically, Physica A: Statistical Mechanics and its Applications, 449, (160), (2016).
  • , Matching–centrality decomposition and the forecasting of new links in networks , Proceedings of the Royal Society B: Biological Sciences, 10.1098/rspb.2015.2702, 283, 1824, (20152702), (2016).
  • , Striations in PageRank-ordered matrices, Social Network Analysis and Mining, 6, 1, (2016).
  • , Triangle-Based Representative Possible Worlds of Uncertain Graphs, Database Systems for Advanced Applications, 10.1007/978-3-319-32049-6_18, (283-298), (2016).
  • , Virtual Community Detection Through the Association between Prime Nodes in Online Social Networks and Its Application to Ranking Algorithms, IEEE Access, 4, (9614), (2016).
  • the 2016 ACM IMC '16 Santa Monica, California, USA Proceedings of the 2016 ACM on Internet Measurement Conference - IMC '16 Internet Measurement Conference ACM Press New York, New York, USA , (2016). 9781450345262 , 10.1145/2987443 20161110083726 http://dl.acm.org/citation.cfm?doid=2987443 Network Growth and Link Prediction Through an Empirical Lens , (2016). 1 15 , 10.1145/2987443.2987452 20161110083727 http://dl.acm.org/citation.cfm?doid=2987443.2987452
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  • , A multilayer approach to multiplexity and link prediction in online geo-social networks, EPJ Data Science, 5, 1, (2016).
  • , Distortive effects of initial‐based name disambiguation on measurements of large‐scale coauthorship networks, Journal of the Association for Information Science and Technology, 67, 6, (1446-1461), (2015).
  • , Methods of Profile Cloning Detection in Online Social Networks, MATEC Web of Conferences, 76, (04013), (2016).
  • , Disconnected, fragmented, or united? a trans-disciplinary review of network science, Applied Network Science, 1, 1, (2016).
  • 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) Nanchang, China 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) IEEE , (2016). 978-1-5090-1915-1 Information Extraction to improve Link Prediction in scientific social networks , (2016). 515 520 7566043 , 10.1109/CSCWD.2016.7566043 http://ieeexplore.ieee.org/document/7566043/
  • , Sampling algorithms for weighted networks, Social Network Analysis and Mining, 6, 1, (2016).
  • , Link prediction using time series of neighborhood-based node similarity scores, Data Mining and Knowledge Discovery, 30, 1, (147), (2016).
  • , Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis, PLOS ONE, 11, 4, (e0154244), (2016).
  • , Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model, BMC Bioinformatics, 17, 1, (2016).
  • , Multi-label classification via learning a unified object-label graph with sparse representation, World Wide Web, 19, 6, (1125), (2016).
  • , Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data, GigaScience, 10.1186/s13742-016-0117-6, 5, 1, (2016).
  • the 22nd ACM SIGKDD International Conference KDD '16 San Francisco, California, USA Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 Knowledge Discovery and Data Mining ACM Press New York, New York, USA , (2016). 9781450342322 , 10.1145/2939672 20160808022742 http://dl.acm.org/citation.cfm?doid=2939672 Meta Structure Computing Relevance in Large Heterogeneous Information Networks , (2016). 1595 1604 , 10.1145/2939672.2939815 20160808022751 http://dl.acm.org/citation.cfm?doid=2939672.2939815 the 22nd ACM SIGKDD International Conference KDD '16 San Francisco, California, USA Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 Knowledge Discovery and Data Mining ACM Press New York, New York, USA , (2016). 9781450342322 , 10.1145/2939672 20160808022742 http://dl.acm.org/citation.cfm?doid=2939672 User Identity Linkage by Latent User Space Modelling , (2016). 1775 1784 , 10.1145/2939672.2939849 20160808022751 http://dl.acm.org/citation.cfm?doid=2939672.2939849 2016 Sixth International Conference on Information Science and Technology (ICIST) Dalian, China 2016 Sixth International Conference on Information Science and Technology (ICIST) IEEE , (2016). 978-1-5090-1224-4 Data exchange similarity based on flow field for link prediction problem , (2016). 84 89 7483390 , 10.1109/ICIST.2016.7483390 http://ieeexplore.ieee.org/document/7483390/ 2016 IEEE 32nd International Conference on Data Engineering (ICDE) Helsinki, Finland 2016 IEEE 32nd International Conference on Data Engineering (ICDE) IEEE , (2016). 978-1-5090-2020-1 SLR: A scalable latent role model for attribute completion and tie prediction in social networks , (2016). 1062 1073 7498313 , 10.1109/ICDE.2016.7498313 http://ieeexplore.ieee.org/document/7498313/
  • , Structural inference for uncertain networks, Physical Review E, 93, 1, (2016).
  • , Estimating multilateral trade behaviors on the world trade web with limited information, Neurocomputing, 210, (66), (2016).
  • , Evidential Missing Link Prediction in Uncertain Social Networks, Information Processing and Management of Uncertainty in Knowledge-Based Systems, 10.1007/978-3-319-40596-4_24, (274-285), (2016).
  • , Literacy: Data Quality, Entities, and Nodes, Network Analysis Literacy, 10.1007/978-3-7091-0741-6_10, (279-311), (2016).
  • , Considering Semantics on the Discovery of Relations in Knowledge Graphs, Knowledge Engineering and Knowledge Management, 10.1007/978-3-319-49004-5_43, (666-680), (2016).
  • , An Evidential Method for Multi-relational Link Prediction in Uncertain Social Networks, Integrated Uncertainty in Knowledge Modelling and Decision Making, 10.1007/978-3-319-49046-5_24, (280-292), (2016).
  • 2016 IEEE 16th International Conference on Data Mining (ICDM) Barcelona, Spain 2016 IEEE 16th International Conference on Data Mining (ICDM) IEEE , (2016). 978-1-5090-5473-2 Large-Scale Embedding Learning in Heterogeneous Event Data , (2016). 907 912 7837924 , 10.1109/ICDM.2016.0111 http://ieeexplore.ieee.org/document/7837924/ the 35th ACM SIGMOD-SIGACT-SIGAI Symposium PODS '16 San Francisco, California, USA Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems - PODS '16 Principles of Database Systems ACM Press New York, New York, USA , (2016). 9781450341912 , 10.1145/2902251 20160616024325 http://dl.acm.org/citation.cfm?doid=2902251 Data Management for Social Networking , (2016). 165 177 , 10.1145/2902251.2902306 20160616024327 http://dl.acm.org/citation.cfm?doid=2902251.2902306
  • , Assessing Antidepressants Using Intelligent Data Monitoring and Mining of Online Fora, IEEE Journal of Biomedical and Health Informatics, 20, 4, (977), (2016).
  • , Identifying and validating personality traits-based homophilies for an egocentric network, Social Network Analysis and Mining, 6, 1, (2016).
  • , A supervised link prediction method for dynamic networks, Journal of Intelligent & Fuzzy Systems, 31, 1, (291), (2016).
  • the Ninth ACM International Conference WSDM '16 San Francisco, California, USA Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM '16 Web Search and Data Mining ACM Press New York, New York, USA , (2016). 9781450337168 , 10.1145/2835776 20160204112154 http://dl.acm.org/citation.cfm?doid=2835776 The Troll-Trust Model for Ranking in Signed Networks , (2016). 447 456 , 10.1145/2835776.2835816 20160204112156 http://dl.acm.org/citation.cfm?doid=2835776.2835816
  • , Estimating the Largest Elements of a Matrix, SIAM Journal on Scientific Computing, 38, 5, (C584), (2016).
  • the Ninth ACM International Conference WSDM '16 San Francisco, California, USA Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM '16 Web Search and Data Mining ACM Press New York, New York, USA , (2016). 9781450337168 , 10.1145/2835776 20160204112154 http://dl.acm.org/citation.cfm?doid=2835776 Centrality-Aware Link Recommendations , (2016). 503 512 , 10.1145/2835776.2835818 20160204112156 http://dl.acm.org/citation.cfm?doid=2835776.2835818
  • , Clustered Matrix Approximation, SIAM Journal on Matrix Analysis and Applications, 37, 4, (1531), (2016).
  • 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) Vietri sul Mare 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) IEEE , (2016). 978-1-5090-0746-2 The influence of hyper-parameters in the infinite relational model , (2016). 1 6 7738908 , 10.1109/MLSP.2016.7738908 http://ieeexplore.ieee.org/document/7738908/
  • , The Impact of Countries’ Roles on the International Photovoltaic Trade Pattern: The Complex Networks Analysis, Sustainability, 8, 4, (313), (2016).
  • , Weight prediction in complex networks based on neighbor set, Scientific Reports, 6, 1, (2016).
  • , A Survey of Link Prediction in Complex Networks, ACM Computing Surveys, 49, 4, (1), (2016).
  • the 25th ACM International CIKM '16 Indianapolis, Indiana, USA Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16 Conference on Information and Knowledge Management ACM Press New York, New York, USA , (2016). 9781450340731 , 10.1145/2983323 20161109032246 http://dl.acm.org/citation.cfm?doid=2983323 Predicting Popularity of Twitter Accounts through the Discovery of Link-Propagating Early Adopters , (2016). 639 648 , 10.1145/2983323.2983859 20161109032251 http://dl.acm.org/citation.cfm?doid=2983323.2983859
  • , Link prediction based on hyperbolic mapping with community structure for complex networks, Physica A: Statistical Mechanics and its Applications, 450, (609), (2016).
  • , Link Prediction in Dynamic Networks Using Graphlet, Machine Learning and Knowledge Discovery in Databases, 10.1007/978-3-319-46128-1_25, (394-409), (2016).
  • Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies - ICTCS '16, (2016).110.1145/2905055.2905149
  • , Evolution and structure of scientific co-publishing network in Korea between 1948–2011, Scientometrics, 107, 1, (27), (2016).
  • , Link Prediction Based on Common-Neighbors for Dynamic Social Network, Procedia Computer Science, 83, (82), (2016).
  • 2016 International Conference on Applied System Innovation (ICASI), (2016).110.1109/ICASI.2016.7539939
  • , Exploiting Information Diffusion Feature for Link Prediction in Sina Weibo, Scientific Reports, 6, 1, (2016).
  • , Improving personalized link prediction by hybrid diffusion, Physica A: Statistical Mechanics and its Applications, 447, (199), (2016).
  • , Toward a better scientific collaboration success prediction model through the feature space expansion, Scientometrics, 10.1007/s11192-016-1999-x, 108, 2, (777-801), (2016).
  • 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) San Francisco, CA, USA 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) IEEE , (2016). 978-1-5090-2846-7 A new topological metric for link prediction in directed, weighted and temporal networks , (2016). 954 959 7752355 , 10.1109/ASONAM.2016.7752355 http://ieeexplore.ieee.org/document/7752355/ 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS) Sapporo, Japan 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS) IEEE , (2016). 978-1-5090-2678-4 Link Prediction in Scientists Collaboration with Author Name and Affiliation , (2016). 233 238 7801645 , 10.1109/SCIS-ISIS.2016.0058 http://ieeexplore.ieee.org/document/7801645/ 2016 49th Hawaii International Conference on System Sciences (HICSS) Koloa, HI, USA 2016 49th Hawaii International Conference on System Sciences (HICSS) IEEE , (2016). 978-0-7695-5670-3 Strategic Network Formation in a Location-Based Social Network: A Topic Modeling Approach , (2016). 5249 5258 7427836 , 10.1109/HICSS.2016.649 http://ieeexplore.ieee.org/document/7427836/
  • , A supervised approach for intra-/inter-community interaction prediction in dynamic social networks, Social Network Analysis and Mining, 6, 1, (2016).
  • 2016 IEEE Trustcom/BigDataSE/I​SPA Tianjin, China 2016 IEEE Trustcom/BigDataSE/ISPA IEEE , (2016). 978-1-5090-3205-1 Topology Prediction Mechanism for Pocket Switched Network Based on Deep Belief Network , (2016). 1811 1817 7847161 , 10.1109/TrustCom.2016.0278 http://ieeexplore.ieee.org/document/7847161/
  • , Eigen-Optimization on Large Graphs by Edge Manipulation, ACM Transactions on Knowledge Discovery from Data, 10, 4, (1), (2016).
  • , Network Structure, Metadata, and the Prediction of Missing Nodes and Annotations, Physical Review X, 6, 3, (2016).
  • , Inferring Implicit Topical Interests on Twitter, Advances in Information Retrieval, 10.1007/978-3-319-30671-1_35, (479-491), (2016).
  • , Power of Bosom Friends, POI Recommendation by Learning Preference of Close Friends and Similar Users, Big Data Analytics and Knowledge Discovery, 10.1007/978-3-319-43946-4_12, (179-192), (2016).
  • , Link prediction based on temporal similarity metrics using continuous action set learning automata, Physica A: Statistical Mechanics and its Applications, 460, (361), (2016).
  • 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) Toulouse, France 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) IEEE , (2016). 978-1-5090-2771-2 Bees Colonies for Meaningfull Communities Detection in Social Medias Network , (2016). 1052 1057 7816960 , 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0164 http://ieeexplore.ieee.org/document/7816960/ the Ninth ACM International Conference WSDM '16 San Francisco, California, USA Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM '16 Web Search and Data Mining ACM Press New York, New York, USA , (2016). 9781450337168 , 10.1145/2835776 20160204112154 http://dl.acm.org/citation.cfm?doid=2835776 Scaling up Link Prediction with Ensembles , (2016). 367 376 , 10.1145/2835776.2835815 20160204112156 http://dl.acm.org/citation.cfm?doid=2835776.2835815
  • , Link Prediction in Weighted Networks: A Weighted Mutual Information Model, PLOS ONE, 11, 2, (e0148265), (2016).
  • , Predicting top-Lmissing links with node and link clustering information in large-scale networks, Journal of Statistical Mechanics: Theory and Experiment, 2016, 8, (083202), (2016).
  • the 22nd ACM SIGKDD International Conference KDD '16 San Francisco, California, USA Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 Knowledge Discovery and Data Mining ACM Press New York, New York, USA , (2016). 9781450342322 , 10.1145/2939672 20160808022742 http://dl.acm.org/citation.cfm?doid=2939672 Structural Neighborhood Based Classification of Nodes in a Network , (2016). 1085 1094 , 10.1145/2939672.2939782 20160808022749 http://dl.acm.org/citation.cfm?doid=2939672.2939782
  • , TeleLink: Link Prediction in Social Network Based on Multiplex Cohesive Structures, Social, Cultural, and Behavioral Modeling, 10.1007/978-3-319-39931-7_17, (174-185), (2016).
  • , Discriminative predicate path mining for fact checking in knowledge graphs, Knowledge-Based Systems, 104, (123), (2016).
  • , Collaborative Completion of Transcription Factor Binding Profiles via Local Sensitive Unified Embedding, IEEE Transactions on NanoBioscience, (1), (2016).
  • , Time-aware link prediction to explore network effects on temporal knowledge evolution, Scientometrics, 10.1007/s11192-016-2003-5, 108, 2, (745-776), (2016).
  • , The power of comments: fostering social interactions in microblog networks, Frontiers of Computer Science, 10, 5, (889), (2016).
  • , Identifying Negative Interactions in Protein-Protein Interaction Network Using Weak Edge-edge Domination Set, Procedia Technology, 24, (1423), (2016).
  • , Link Prediction in Complex Networks, Advanced Methods for Complex Network Analysis, 10.4018/978-1-4666-9964-9.ch003, (58-97)
  • , Link Prediction via Multi-hashing Framework, Social, Cultural, and Behavioral Modeling, 10.1007/978-3-319-39931-7_16, (162-173), (2016).
  • 2016 24th Telecommunications Forum (TELFOR) Belgrade, Serbia 2016 24th Telecommunications Forum (TELFOR) IEEE , (2016). 978-1-5090-4086-5 Greedy reduction of navigation time in random search processes , (2016). 1 4 7818724 , 10.1109/TELFOR.2016.7818724 http://ieeexplore.ieee.org/document/7818724/
  • , Assessing Low-Intensity Relationships in Complex Networks, PLOS ONE, 11, 4, (e0152536), (2016).
  • , Similarity Metrics from Social Network Analysis for Content Recommender Systems, Case-Based Reasoning Research and Development, 10.1007/978-3-319-47096-2_14, (203-217), (2016).
  • , Link Prediction via Sparse Gaussian Graphical Model, Mathematical Problems in Engineering, 2016, (1), (2016).
  • , Using weak ties to understand the resource usage and sharing patterns of a professional learning community, Social Network Analysis and Mining, 6, 1, (2016).
  • 2016 23rd International Conference on Pattern Recognition (ICPR) Cancun 2016 23rd International Conference on Pattern Recognition (ICPR) IEEE , (2016). 978-1-5090-4847-2 Exploiting social and mobility patterns for friendship prediction in location-based social networks , (2016). 2526 2531 7900016 , 10.1109/ICPR.2016.7900016 http://ieeexplore.ieee.org/document/7900016/
  • , Leveraging Neighbor Attributes for Classification in Sparsely Labeled Networks, ACM Transactions on Knowledge Discovery from Data, 11, 1, (1), (2016).
  • , Predicting links in ego-networks using temporal information, EPJ Data Science, 5, 1, (2016).
  • , Factorization threshold models for scale-free networks generation, Computational Social Networks, 3, 1, (2016).
  • , A supervised learning approach to link prediction in Twitter, Social Network Analysis and Mining, 6, 1, (2016).
  • the 22nd ACM SIGKDD International Conference KDD '16 San Francisco, California, USA Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 Knowledge Discovery and Data Mining ACM Press New York, New York, USA , (2016). 9781450342322 , 10.1145/2939672 20160808022742 http://dl.acm.org/citation.cfm?doid=2939672 Efficient Processing of Network Proximity Queries via Chebyshev Acceleration , (2016). 1515 1524 , 10.1145/2939672.2939828 20160808022750 http://dl.acm.org/citation.cfm?doid=2939672.2939828
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  • 2016 IEEE High Performance Extreme Computing Conference (HPEC) Waltham, MA, USA 2016 IEEE High Performance Extreme Computing Conference (HPEC) IEEE , (2016). 978-1-5090-3525-0 From NoSQL Accumulo to NewSQL Graphulo: Design and utility of graph algorithms inside a BigTable database , (2016). 1 9 7761577 , 10.1109/HPEC.2016.7761577 http://ieeexplore.ieee.org/document/7761577/
  • , Put Three and Three Together, ACM Transactions on Knowledge Discovery from Data, 10, 3, (1), (2016).
  • , Bounded link prediction in very large networks, Physica A: Statistical Mechanics and its Applications, 457, (202), (2016).
  • , Link prediction algorithm based on the Choquet fuzzy integral, Intelligent Data Analysis, 20, 4, (809), (2016).
  • , Inferring Social Strength from Spatiotemporal Data, ACM Transactions on Database Systems, 41, 1, (1), (2016).
  • , Predictive Analytics of Social Networks, Business Intelligence, 10.4018/978-1-4666-9562-7.ch056, (1080-1116), (2016).
  • the 12th International Symposium OpenSym '16 Berlin, Germany Proceedings of the 12th International Symposium on Open Collaboration - OpenSym '16 Open Collaboration ACM Press New York, New York, USA , (2016). 9781450344517 , 10.1145/2957792 20161020111943 http://dl.acm.org/citation.cfm?doid=2957792 Evaluating and Improving Navigability of Wikipedia A Comparative Study of Eight Language Editions , (2016). 1 10 , 10.1145/2957792.2957813 20161020111944 http://dl.acm.org/citation.cfm?doid=2957792.2957813
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  • , Unsupervised link prediction in evolving abnormal medical parameter networks, International Journal of Machine Learning and Cybernetics, 7, 1, (145), (2016).
  • , Efficient network disintegration under incomplete information: the comic effect of link prediction, Scientific Reports, 6, 1, (2016).
  • , Finding Influential Users in Social Media Using Association Rule Learning, Entropy, 18, 12, (164), (2016).
  • 2016 IEEE 41st Conference on Local Computer Networks (LCN) Dubai 2016 IEEE 41st Conference on Local Computer Networks (LCN) IEEE , (2016). 978-1-5090-2054-6 Inferring Future Links in Large Scale Networks , (2016). 244 252 7796796 , 10.1109/LCN.2016.52 http://ieeexplore.ieee.org/document/7796796/ the 22nd ACM SIGKDD International Conference KDD '16 San Francisco, California, USA Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 Knowledge Discovery and Data Mining ACM Press New York, New York, USA , (2016). 9781450342322 , 10.1145/2939672 20160808022742 http://dl.acm.org/citation.cfm?doid=2939672 When Recommendation Goes Wrong Anomalous Link Discovery in Recommendation Networks , (2016). 569 578 , 10.1145/2939672.2939734 20160808022746 http://dl.acm.org/citation.cfm?doid=2939672.2939734
  • , The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems, PLOS ONE, 11, 5, (e0154848), (2016).
  • , Conceptualization of science using collaboration and competences, The Electronic Library, 34, 1, (2), (2016).
  • 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) Rajshahi, Bangladesh 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) IEEE , (2016). 978-1-5090-5785-6 Developing a framework for analyzing social networks to identify human behaviours , (2016). 1 4 7879589 , 10.1109/ICECTE.2016.7879589 http://ieeexplore.ieee.org/document/7879589/
  • , Enumerating Maximal Isolated Cliques Based on Vertex-Dependent Connection Lower Bound, Machine Learning and Data Mining in Pattern Recognition, 10.1007/978-3-319-41920-6_45, (569-583), (2016).
  • , Link prediction based on path entropy, Physica A: Statistical Mechanics and its Applications, 456, (294), (2016).
  • , Accuracy estimation of link-based similarity measures and its application, Frontiers of Computer Science, 10, 1, (113), (2016).
  • , Technology roadmapping for competitive technical intelligence, Technological Forecasting and Social Change, 110, (175), (2016).
  • , Predicting drug target interactions using meta-path-based semantic network analysis, BMC Bioinformatics, 17, 1, (2016).
  • , Model-Based Collaborative Personalized Recommendation on Signed Social Rating Networks, ACM Transactions on Internet Technology, 16, 3, (1), (2016).
  • 2016 IEEE 32nd International Conference on Data Engineering (ICDE) Helsinki, Finland 2016 IEEE 32nd International Conference on Data Engineering (ICDE) IEEE , (2016). 978-1-5090-2020-1 Spatial influence - measuring followship in the real world , (2016). 529 540 7498268 , 10.1109/ICDE.2016.7498268 http://ieeexplore.ieee.org/document/7498268/ 2016 IEEE International Congress on Big Data (BigData Congress) San Francisco, CA, USA 2016 IEEE International Congress on Big Data (BigData Congress) IEEE , (2016). 978-1-5090-2622-7 Analyzing Future Nodes in a Knowledge Network , (2016). 357 360 7584963 , 10.1109/BigDataCongress.2016.57 http://ieeexplore.ieee.org/document/7584963/
  • , Mu-En: Multi-path of Entity Recommendation Based on Path Similarity, Human Centered Computing, 10.1007/978-3-319-31854-7_31, (344-354), (2016).
  • 2016 8th International Conference on Communication Systems and Networks (COMSNETS) Bangalore, India 2016 8th International Conference on Communication Systems and Networks (COMSNETS) IEEE , (2016). 978-1-4673-9622-6 Social interaction in the Flickr social network , (2016). 1 6 7439975 , 10.1109/COMSNETS.2016.7439975 http://ieeexplore.ieee.org/document/7439975/
  • , A Review of Relational Machine Learning for Knowledge Graphs, Proceedings of the IEEE, 104, 1, (11), (2016).
  • 2016 9th International Symposium on Computational Intelligence and Design (ISCID) Hangzhou 2016 9th International Symposium on Computational Intelligence and Design (ISCID) IEEE , (2016). 978-1-5090-3558-8 Modeling and Correlation Analysis between Complex Networks , (2016). 277 280 7830842 , 10.1109/ISCID.2016.2073 http://ieeexplore.ieee.org/document/7830842/ the 22nd ACM SIGKDD International Conference KDD '16 San Francisco, California, USA Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 Knowledge Discovery and Data Mining ACM Press New York, New York, USA , (2016). 9781450342322 , 10.1145/2939672 20160808022742 http://dl.acm.org/citation.cfm?doid=2939672 Structural Deep Network Embedding , (2016). 1225 1234 , 10.1145/2939672.2939753 20160808022749 http://dl.acm.org/citation.cfm?doid=2939672.2939753 2016 IEEE International Conference on Big Data (Big Data) Washington DC,USA 2016 IEEE International Conference on Big Data (Big Data) IEEE , (2016). 978-1-4673-9005-7 Automated classification of extremist Twitter accounts using content-based and network-based features , (2016). 2545 2549 7840895 , 10.1109/BigData.2016.7840895 http://ieeexplore.ieee.org/document/7840895/
  • , The power of indirect ties, Computer Communications, 73, (188), (2016).
  • the 25th ACM International CIKM '16 Indianapolis, Indiana, USA Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16 Conference on Information and Knowledge Management ACM Press New York, New York, USA , (2016). 9781450340731 , 10.1145/2983323 20161109032246 http://dl.acm.org/citation.cfm?doid=2983323 Fully Dynamic Shortest-Path Distance Query Acceleration on Massive Networks , (2016). 1533 1542 , 10.1145/2983323.2983731 20161109032253 http://dl.acm.org/citation.cfm?doid=2983323.2983731 2016 IEEE 16th International Conference on Data Mining (ICDM) Barcelona, Spain 2016 IEEE 16th International Conference on Data Mining (ICDM) IEEE , (2016). 978-1-5090-5473-2 Mining Statistically Significant Attribute Associations in Attributed Graphs , (2016). 991 996 7837938 , 10.1109/ICDM.2016.0125 http://ieeexplore.ieee.org/document/7837938/
  • , FriendBurst: Ranking people who get friends fast in a short time, Neurocomputing, 210, (116), (2016).
  • 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom) Atlanta, GA, USA 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom) IEEE , (2016). 978-1-5090-3936-4 Evaluating Link Prediction Accuracy in Dynamic Networks with Added and Removed Edges , (2016). 377 384 7723717 , 10.1109/BDCloud-SocialCom-SustainCom.2016.63 http://ieeexplore.ieee.org/document/7723717/
  • , Comparison of Local Information Indices Applied in Resting State Functional Brain Network Connectivity Prediction, Frontiers in Neuroscience, 10, (2016).
  • , Link prediction with node clustering coefficient, Physica A: Statistical Mechanics and its Applications, 452, (1), (2016).
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  • , Mining social networks for anomalies: Methods and challenges, Journal of Network and Computer Applications, 10.1016/j.jnca.2016.02.021, 68, (213-229), (2016).
  • , Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction, IEEE Transactions on Computational Social Systems, 3, 3, (113), (2016).
  • , Transfer Learning to Infer Social Ties across Heterogeneous Networks, ACM Transactions on Information Systems, 34, 2, (1), (2016).
  • , Who should you follow? Combining learning to rank with social influence for informative friend recommendation, Decision Support Systems, 90, (33), (2016).
  • 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) Chengdu, China 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) IEEE , (2016). 978-1-5090-5880-8 Deep Learning Based Link Prediction with Social Pattern and External Attribute Knowledge in Bibliographic Networks , (2016). 815 821 7917198 , 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.170 http://ieeexplore.ieee.org/document/7917198/ 2016 2nd International Conference on Information Management (ICIM) London, United Kingdom 2016 2nd International Conference on Information Management (ICIM) IEEE , (2016). 978-1-5090-1470-5 978-1-5090-1471-2 Relationships between network's structure features and link prediction algorithms , (2016). 64 72 7477535 , 10.1109/INFOMAN.2016.7477535 http://ieeexplore.ieee.org/document/7477535/
  • , Link Prediction in a Semi-bipartite Network for Recommendation, Intelligent Information and Database Systems, 10.1007/978-3-662-49390-8_12, (127-135), (2016).
  • , A Friend Like Me: Modeling Network Formation in a Location-Based Social Network, Journal of Management Information Systems, 33, 4, (1008), (2016).
  • 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA) Qazvin, Iran 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA) IEEE , (2016). 978-1-4673-8704-0 A new similarity measure based on item proximity and closeness for collaborative filtering recommendation , (2016). 445 450 7483204 , 10.1109/ICCIAutom.2016.7483204 http://ieeexplore.ieee.org/document/7483204/ 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) San Francisco, CA, USA 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) IEEE , (2016). 978-1-5090-2846-7 A holistic approach for predicting links in coevolving multiplex networks , (2016). 1079 1086 7752375 , 10.1109/ASONAM.2016.7752375 http://ieeexplore.ieee.org/document/7752375/
  • , S-Rank: A Supervised Ranking Framework for Relationship Prediction in Heterogeneous Information Networks, Trends in Applied Knowledge-Based Systems and Data Science, 10.1007/978-3-319-42007-3_26, (305-319), (2016).
  • , Adaptive degree penalization for link prediction, Journal of Computational Science, 13, (1), (2016).
  • the 31st IEEE/ACM International Conference ASE 2016 Singapore, Singapore Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering - ASE 2016 Automated Software Engineering ACM Press New York, New York, USA , (2016). 9781450338455 , 10.1145/2970276 20160826081113 http://dl.acm.org/citation.cfm?doid=2970276 Predicting semantically linkable knowledge in developer online forums via convolutional neural network , (2016). 51 62 , 10.1145/2970276.2970357 20160826081113 http://dl.acm.org/citation.cfm?doid=2970276.2970357
  • , Enhancing Link Prediction Using Gradient Boosting Features, Intelligent Computing Theories and Application, 10.1007/978-3-319-42294-7_7, (81-92), (2016).
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  • 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) San Jose, CA, USA 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) IEEE , (2016). 978-1-5090-4459-7 Link Prediction in Social Networks with Edge Aging , (2016). 606 613 7814658 , 10.1109/ICTAI.2016.0098 http://ieeexplore.ieee.org/document/7814658/
  • , The Prediction of Venture Capital Co-Investment Based on Structural Balance Theory, IEEE Transactions on Knowledge and Data Engineering, 28, 2, (537), (2016).
  • , Tag-based personalized recommendation in social media services, Multimedia Tools and Applications, 75, 21, (13299), (2016).
  • 2016 IEEE International Conference on Web Services (ICWS) San Francisco, CA, USA 2016 IEEE International Conference on Web Services (ICWS) IEEE , (2016). 978-1-5090-2675-3 User Preference Based Link Inference for Social Network , (2016). 188 195 7558001 , 10.1109/ICWS.2016.32 http://ieeexplore.ieee.org/document/7558001/
  • , Cluster-level trust prediction based on multi-modal social networks, Neurocomputing, 210, (206), (2016).
  • the Ninth ACM International Conference WSDM '16 San Francisco, California, USA Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM '16 Web Search and Data Mining ACM Press New York, New York, USA , (2016). 9781450337168 , 10.1145/2835776 20160204112154 http://dl.acm.org/citation.cfm?doid=2835776 Improving Website Hyperlink Structure Using Server Logs , (2016). 615 624 , 10.1145/2835776.2835832 20160204112156 http://dl.acm.org/citation.cfm?doid=2835776.2835832 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) San Francisco, CA, USA 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) IEEE , (2016). 978-1-5090-2846-7 Assessment of effectiveness of content models for approximating Twitter social connection structures , (2016). 1071 1078 7752374 , 10.1109/ASONAM.2016.7752374 http://ieeexplore.ieee.org/document/7752374/
  • , Predicting investor funding behavior using crunchbase social network features, Internet Research, 26, 1, (74), (2016).
  • 2016 IEEE 16th International Conference on Data Mining (ICDM) Barcelona, Spain 2016 IEEE 16th International Conference on Data Mining (ICDM) IEEE , (2016). 978-1-5090-5473-2 Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties , (2016). 781 786 7837903 , 10.1109/ICDM.2016.0090 http://ieeexplore.ieee.org/document/7837903/ 2016 International Conference on Machine Learning and Cybernetics (ICMLC) Jeju Island, South Korea 2016 International Conference on Machine Learning and Cybernetics (ICMLC) IEEE , (2016). 978-1-5090-0390-7 Link prediction based on dynamic weighted social attribute network , (2016). 183 188 7860898 , 10.1109/ICMLC.2016.7860898 http://ieeexplore.ieee.org/document/7860898/
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  • , A novel green algorithm for sampling complex networks, Journal of Network and Computer Applications, 59, (55), (2016).
  • the 25th International Conference WWW '16 Montr&#233;al, Qu&#233;bec, Canada Proceedings of the 25th International Conference on World Wide Web - WWW '16 World Wide Web ACM Press New York, New York, USA , (2016). 9781450341431 , 10.1145/2872427 20170123033132 http://dl.acm.org/citation.cfm?doid=2872427 The Effect of Recommendations on Network Structure , (2016). 1157 1167 , 10.1145/2872427.2883040 20170123033155 http://dl.acm.org/citation.cfm?doid=2872427.2883040
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  • , Predicting missing links and identifying spurious links via likelihood analysis, Scientific Reports, 6, 1, (2016).
  • , An Approach to Cold-Start Link Prediction: Establishing Connections between Non-Topological and Topological Information, IEEE Transactions on Knowledge and Data Engineering, 28, 11, (2857), (2016).
  • , Scalable Detection of Overlapping Communities and Role Assignments in Networks via Bayesian Probabilistic Generative Affiliation Modeling, On the Move to Meaningful Internet Systems: OTM 2016 Conferences, 10.1007/978-3-319-48472-3_6, (99-117), (2016).
  • , Parallel collective factorization for modeling large heterogeneous networks, Social Network Analysis and Mining, 6, 1, (2016).
  • , A survey of data mining and social network analysis based anomaly detection techniques, Egyptian Informatics Journal, 17, 2, (199), (2016).
  • the 2015 ACM SIGMOD International Conference SIGMOD '15 Melbourne, Victoria, Australia Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15 Management of Data ACM Press New York, New York, USA , (2015). 9781450327589 , 10.1145/2723372 20150601121643 http://dl.acm.org/citation.cfm?doid=2723372 Efficient Enumeration of Maximal k-Plexes , (2015). 431 444 , 10.1145/2723372.2746478 20150601121644 http://dl.acm.org/citation.cfm?doid=2723372.2746478
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  • , Prediction in complex systems: The case of the international trade network, Physica A: Statistical Mechanics and its Applications, 436, (188), (2015).
  • , Harmony Assumptions in Information Retrieval and Social Networks, The Computer Journal, 58, 11, (2982), (2015).
  • , Predicting Positive and Negative Relationships in Large Social Networks, PLOS ONE, 10, 6, (e0129530), (2015).
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  • , Triadic Closure Pattern Analysis and Prediction in Social Networks, IEEE Transactions on Knowledge and Data Engineering, 27, 12, (3374), (2015).
  • IEEE INFOCOM 2015 - IEEE Conference on Computer Communications Kowloon, Hong Kong 2015 IEEE Conference on Computer Communications (INFOCOM) IEEE , (2015). 978-1-4799-8381-0 Cliques in hyperbolic random graphs , (2015). 1544 1552 7218533 , 10.1109/INFOCOM.2015.7218533 http://ieeexplore.ieee.org/document/7218533/
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  • , Predicting Community Evolution in Social Networks, Entropy, 17, 12, (3053), (2015).
  • , Social Discovery: Exploring the Correlation Among Three-Dimensional Social Relationships, IEEE Transactions on Computational Social Systems, 2, 3, (77), (2015).
  • , A link-based approach to semantic relation analysis, Neurocomputing, 10.1016/j.neucom.2014.12.011, 154, (127-138), (2015).
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  • , Whether information network supplements friendship network, Physica A: Statistical Mechanics and its Applications, 419, (301), (2015).
  • , A New Approach to Link Prediction in Gene Regulatory Networks, Intelligent Data Engineering and Automated Learning – IDEAL 2015, 10.1007/978-3-319-24834-9_47, (404-415), (2016).
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  • , Location Sensitive Friend Recommendation in Social Network, Web Technologies and Applications, 10.1007/978-3-319-25255-1_26, (316-327), (2015).
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  • , Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents, Technological Forecasting and Social Change, 10.1016/j.techfore.2015.07.022, 100, (317-329), (2015).
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  • 2015 International Congress on Technology, Communication and Knowledge (ICTCK) Mashhad, Iran 2015 International Congress on Technology, Communication and Knowledge (ICTCK) IEEE , (2015). 978-1-4673-9762-9 A recommender system based on local topology features and message rate , (2015). 276 280 7582682 , 10.1109/ICTCK.2015.7582682 http://ieeexplore.ieee.org/document/7582682/
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  • , Predicting missing links via structural similarity, International Journal of Modern Physics B, 29, 15, (1550095), (2015).
  • the Eighth ACM International Conference WSDM '15 Shanghai, China Proceedings of the Eighth ACM International Conference on Web Search and Data Mining - WSDM '15 Web Search and Data Mining ACM Press New York, New York, USA , (2015). 9781450333177 , 10.1145/2684822 20150128090605 http://dl.acm.org/citation.cfm?doid=2684822 Negative Link Prediction in Social Media , (2015). 87 96 , 10.1145/2684822.2685295 20150128090605 http://dl.acm.org/citation.cfm?doid=2684822.2685295
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  • , Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care, IEEE Journal of Biomedical and Health Informatics, 19, 1, (210), (2015).
  • the 2015 ACM COSN '15 Palo Alto, California, USA Proceedings of the 2015 ACM on Conference on Online Social Networks - COSN '15 Conference on Online Social Networks ACM Press New York, New York, USA , (2015). 9781450339513 , 10.1145/2817946 20151027081720 http://dl.acm.org/citation.cfm?doid=2817946 Information Seeking and Responding Networks in Physical Gatherings A Case Study of Academic Conferences in Twitter , (2015). 197 208 , 10.1145/2817946.2817960 20151027081721 http://dl.acm.org/citation.cfm?doid=2817946.2817960 2015 IEEE International Conference on Data Mining (ICDM) Atlantic City, NJ, USA 2015 IEEE International Conference on Data Mining IEEE , (2015). 978-1-4673-9504-5 Diamond Sampling for Approximate Maximum All-Pairs Dot-Product (MAD) Search , (2015). 11 20 7373305 , 10.1109/ICDM.2015.46 http://ieeexplore.ieee.org/document/7373305/ 2015 Annual IEEE India Conference (INDICON) New Delhi, India 2015 Annual IEEE India Conference (INDICON) IEEE , (2015). 978-1-4673-7399-9 Detecting anomalies in Online Social Networks using graph metrics , (2015). 1 6 7443800 , 10.1109/INDICON.2015.7443800 http://ieeexplore.ieee.org/document/7443800/
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  • , Proposing Ties in a Dense Hypergraph of Academics, Social Informatics, 10.1007/978-3-319-27433-1_15, (209-226), (2015).
  • , Topological data analysis of contagion maps for examining spreading processes on networks, Nature Communications, 6, 1, (2015).
  • , A Novel Data-Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin, IEEE Journal of Biomedical and Health Informatics, 19, 1, (389), (2015).
  • , Proposing a new friend recommendation method, FRUTAI, to enhance social media providers' performance, Decision Support Systems, 79, (46), (2015).
  • 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) Vietri sul Mare, Italy 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) IEEE , (2015). 978-1-5090-0163-7 Hybrid Swarm Based Method for Link Prediction in Social Networks , (2015). 974 981 7372237 , 10.1109/ICTAI.2015.140 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7372237
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  • , Effective Citation Recommendation by Unbiased Reference Priority Recognition, Web Technologies and Applications, 10.1007/978-3-319-25255-1_44, (536-547), (2015).
  • 2015 31st IEEE International Conference on Data Engineering Workshops (ICDEW) Seoul, South Korea 2015 31st IEEE International Conference on Data Engineering Workshops IEEE , (2015). 978-1-4799-8442-8 Traversal optimizations and analysis for large graph clustering , (2015). 260 264 7129587 , 10.1109/ICDEW.2015.7129587 http://ieeexplore.ieee.org/document/7129587/ 2014 International Joint Conference on Neural Networks (IJCNN) Beijing, China 2014 International Joint Conference on Neural Networks (IJCNN) IEEE , (2014). 978-1-4799-1484-5 978-1-4799-6627-1 Max-margin latent feature relational models for entity-attribute networks , (2014). 1667 1674 6889508 , 10.1109/IJCNN.2014.6889508 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6889508
  • , Dynamic motifs in socio-economic networks, EPL (Europhysics Letters), 108, 5, (58001), (2014).
  • , Hierarchical Block Structures and High-Resolution Model Selection in Large Networks, Physical Review X, 4, 1, (2014).
  • Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization, (2014).110.1109/ICRITO.2014.7014756
  • , Vertex collocation profiles: theory, computation, and results, SpringerPlus, 3, 1, (116), (2014).
  • , Link Prediction in Complex Networks: A Mutual Information Perspective, PLoS ONE, 9, 9, (e107056), (2014).
  • , A Spread Willingness Computing-Based Information Dissemination Model, The Scientific World Journal, 2014, (1), (2014).
  • 2014 11th Annual Conference on Wireless On-demand Network Systems and Services (WONS) Obergurgl, Austria 2014 11th Annual Conference on Wireless On-demand Network Systems and Services (WONS) IEEE , (2014). 978-1-4799-4937-3 Beyond contact predictions in mobile opportunistic networks , (2014). 65 72 6814723 , 10.1109/WONS.2014.6814723 http://ieeexplore.ieee.org/document/6814723/ 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) China 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) IEEE , (2014). 978-1-4799-5877-1 Link discovery in social networks , (2014). 898 905 6921692 , 10.1109/ASONAM.2014.6921692 http://ieeexplore.ieee.org/document/6921692/ 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS) Shenzhen, China 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems IEEE , (2014). 978-1-4799-4720-1 978-1-4799-4719-5 Link prediction in Sina Microblog using comprehensive features and improved SVM algorithm , (2014). 18 22 7175696 , 10.1109/CCIS.2014.7175696 http://ieeexplore.ieee.org/document/7175696/
  • , Link Prediction in Directed Network and Its Application in Microblog, Mathematical Problems in Engineering, 2014, (1), (2014).
  • , MVCWalker: Random Walk-Based Most Valuable Collaborators Recommendation Exploiting Academic Factors, IEEE Transactions on Emerging Topics in Computing, 2, 3, (364), (2014).
  • , Revealing Missing Parts of the Interactome via Link Prediction, PLoS ONE, 9, 3, (e90073), (2014).
  • the 23rd International Conference WWW '14 Companion Seoul, Korea Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion World Wide Web ACM Press New York, New York, USA , (2014). 9781450327459 , 10.1145/2567948 20160205120955 http://dl.acm.org/citation.cfm?doid=2567948 A novel link prediction approach for scale-free networks , (2014). 1333 1338 , 10.1145/2567948.2580049 20160205120958 http://dl.acm.org/citation.cfm?doid=2567948.2580049
  • , Link Prediction Based on Clustering Coefficient, Applied Physics, 04, 06, (101), (2014).
  • 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Chengdu, China 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) IEEE , (2014). 978-1-4799-4717-1 Graph-based personalized recommendation in social tagging systems , (2014). 1 6 6890593 , 10.1109/ICMEW.2014.6890593 http://ieeexplore.ieee.org/document/6890593/
  • , Recommending research collaborations using link prediction and random forest classifiers, Scientometrics, 101, 2, (1461), (2014).
  • , Exploring and inferring user–user pseudo‐friendship for sentiment analysis with heterogeneous networks, Statistical Analysis and Data Mining: The ASA Data Science Journal, 7, 4, (308-321), (2014).
  • , Social Network Analysis and Critical Realism, Journal for the Theory of Social Behaviour, 44, 3, (306-325), (2013).
  • the First International Workshop WISMM '14 Orlando, Florida, USA Proceedings of the First International Workshop on Internet-Scale Multimedia Management - WISMM '14 Internet-Scale Multimedia Management ACM Press New York, New York, USA , (2014). 9781450331579 , 10.1145/2661714 20141107100524 http://dl.acm.org/citation.cfm?doid=2661714 Towards Storytelling by Extracting Social Information from OSN Photo's Metadata , (2014). 15 20 , 10.1145/2661714.2661721 20141107100524 http://dl.acm.org/citation.cfm?doid=2661714.2661721
  • , Analyzing future communities in growing citation networks, Knowledge-Based Systems, 69, (34), (2014).
  • 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA) Doha, Qatar 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA) IEEE , (2014). 978-1-4799-7100-8 Feature based link prediction , (2014). 523 527 7073243 , 10.1109/AICCSA.2014.7073243 http://ieeexplore.ieee.org/document/7073243/
  • , A multi-theoretical kernel-based approach to social network-based recommendation, Decision Support Systems, 65, (95), (2014).
  • 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD) Hsinchu, Taiwan Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD) IEEE , (2014). 978-1-4799-3776-9 Link prediction based on time-varied weight in co-authorship network , (2014). 706 709 6846931 , 10.1109/CSCWD.2014.6846931 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6846931
  • , Recommendation Algorithm based on Link Prediction and Domain Knowledge in Retail Transactions, Procedia Computer Science, 31, (875), (2014).
  • , Using clustering coefficient to construct weighted networks for supervised link prediction, Social Network Analysis and Mining, 4, 1, (2014).
  • 2014 International Conference on Data Science and Advanced Analytics (DSAA) Shanghai, China 2014 International Conference on Data Science and Advanced Analytics (DSAA) IEEE , (2014). 978-1-4799-6991-3 Link prediction and threads in email networks , (2014). 470 476 7058114 , 10.1109/DSAA.2014.7058114 http://ieeexplore.ieee.org/document/7058114/ 2014 IEEE 30th International Conference on Data Engineering (ICDE) Chicago, IL, USA 2014 IEEE 30th International Conference on Data Engineering IEEE , (2014). 978-1-4799-2555-1 Evaluating multi-way joins over discounted hitting time , (2014). 724 735 6816695 , 10.1109/ICDE.2014.6816695 http://ieeexplore.ieee.org/document/6816695/ the 13th Workshop WPES '14 Scottsdale, Arizona, USA Proceedings of the 13th Workshop on Privacy in the Electronic Society - WPES '14 Privacy in the Electronic Society ACM Press New York, New York, USA , (2014). 9781450331487 , 10.1145/2665943 20141107100519 http://dl.acm.org/citation.cfm?doid=2665943 Exploiting Users' Inconsistent Preferences in Online Social Networks to Discover Private Friendship Links , (2014). 59 68 , 10.1145/2665943.2665956 20141107100520 http://dl.acm.org/citation.cfm?doid=2665943.2665956 2014 10th International Conference on Natural Computation (ICNC) Xiamen 2014 10th International Conference on Natural Computation (ICNC) IEEE , (2014). 978-1-4799-5151-2 Accurate and fast link prediction in complex networks , (2014). 653 657 6975913 , 10.1109/ICNC.2014.6975913 http://ieeexplore.ieee.org/document/6975913/
  • , HALLP: A Hybrid Active Learning Approach to Link Prediction Task, Journal of Computers, 9, 3, (2014).
  • , Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach, New Generation Computing, 32, 3-4, (213), (2014).
  • , Improved Network-Based Recommendation Algorithm, iBusiness, 06, 03, (109), (2014).
  • 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) China 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) IEEE , (2014). 978-1-4799-5877-1 Recommendation in Academia: A joint multi-relational model , (2014). 566 571 6921643 , 10.1109/ASONAM.2014.6921643 http://ieeexplore.ieee.org/document/6921643/
  • , The social distributional hypothesis: a pragmatic proxy for homophily in online social networks, Social Network Analysis and Mining, 4, 1, (2014).
  • 2014 11th World Congress on Intelligent Control and Automation (WCICA) Shenyang, China Proceeding of the 11th World Congress on Intelligent Control and Automation IEEE , (2014). 978-1-4799-5825-2 Microblog friends automatic clustering framework based on similarity measurement , (2014). 5152 5157 7053592 , 10.1109/WCICA.2014.7053592 http://ieeexplore.ieee.org/document/7053592/
  • , Developments in the theory of randomized shortest paths with a comparison of graph node distances, Physica A: Statistical Mechanics and its Applications, 393, (600), (2014).
  • 2014 4th International Symposium ISKO-Maghreb: Concepts and Tools for knowledge Management (ISKO-Maghreb) Algiers, Algeria 2014 4th International Symposium ISKO-Maghreb: Concepts and Tools for knowledge Management (ISKO-Maghreb) IEEE , (2014). 978-1-4799-7508-2 978-1-4799-7507-5 WMPLP: A model for link prediction in heterogeneous social networks , (2014). 1 4 7033447 , 10.1109/ISKO-Maghreb.2014.7033447 http://ieeexplore.ieee.org/document/7033447/
  • , Patent keyword network analysis for improving technology development efficiency, Technological Forecasting and Social Change, 83, (170), (2014).
  • 2014 6th International Conference on Computational Aspects of Social Networks (CASoN) Porto, Portugal 2014 6th International Conference on Computational Aspects of Social Networks IEEE , (2014). 978-1-4799-5940-2 978-1-4799-5939-6 Modeling dynamics of social networks: A survey , (2014). 49 54 6920421 , 10.1109/CASoN.2014.6920421 http://ieeexplore.ieee.org/document/6920421/ 2014 International Conference on Data Science and Advanced Analytics (DSAA) Shanghai, China 2014 International Conference on Data Science and Advanced Analytics (DSAA) IEEE , (2014). 978-1-4799-6991-3 Recommending missing citations for newly granted patents , (2014). 442 448 7058110 , 10.1109/DSAA.2014.7058110 http://ieeexplore.ieee.org/document/7058110/
  • , Memory effects induce structure in social networks with activity-driven agents, Journal of Statistical Mechanics: Theory and Experiment, 2014, 9, (P09009), (2014).
  • , Latent self-exciting point process model for spatial-temporal networks, Discrete and Continuous Dynamical Systems - Series B, 19, 5, (1335), (2014).
  • , Power grid complex network evolutions for the smart grid, Physica A: Statistical Mechanics and its Applications, 396, (248), (2014).
  • , Generalized graphlet kernels for probabilistic inference in sparse graphs, Network Science, 10.1017/nws.2014.14, 2, 02, (254-276), (2014).
  • , Recommendation in an Evolving Service Ecosystem Based on Network Prediction, IEEE Transactions on Automation Science and Engineering, 11, 3, (906), (2014).
  • 2014 IEEE 30th International Conference on Data Engineering (ICDE) Chicago, IL, USA 2014 IEEE 30th International Conference on Data Engineering IEEE , (2014). 978-1-4799-2555-1 LinkSCAN*: Overlapping community detection using the link-space transformation , (2014). 292 303 6816659 , 10.1109/ICDE.2014.6816659 http://ieeexplore.ieee.org/document/6816659/ 2014 IEEE International Conference on Big Data (Big Data) Washington, DC, USA 2014 IEEE International Conference on Big Data (Big Data) IEEE , (2014). 978-1-4799-5666-1 Why name ambiguity resolution matters for scholarly big data research , (2014). 1 6 7004345 , 10.1109/BigData.2014.7004345 http://ieeexplore.ieee.org/document/7004345/
  • , Maiter: An Asynchronous Graph Processing Framework for Delta-Based Accumulative Iterative Computation, IEEE Transactions on Parallel and Distributed Systems, 25, 8, (2091), (2014).
  • , Investigating How User's Activities in Both Virtual and Physical World Impact Each Other Leveraging LBSN Data, International Journal of Distributed Sensor Networks, 10, 2, (461780), (2014).
  • 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS) Bangalore, India 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS) IEEE , (2014). 978-1-4799-3635-9 Heterogeneous link prediction based on multi relational community information , (2014). 1 4 6734932 , 10.1109/COMSNETS.2014.6734932 http://ieeexplore.ieee.org/document/6734932/
  • , Matching of communities and frequent conceptual links, Social Network Analysis and Mining, 4, 1, (2014).
  • , A lightweight privacy preserving SMS-based recommendation system for mobile users, Knowledge and Information Systems, 40, 1, (49), (2014).
  • , Recommender systems based on collaborative filtering and resource allocation, Social Network Analysis and Mining, 4, 1, (2014).
  • , Querying dynamic communities in online social networks, Journal of Zhejiang University SCIENCE C, 15, 2, (81), (2014).
  • , An evolutionary algorithm approach to link prediction in dynamic social networks, Journal of Computational Science, 5, 5, (750), (2014).
  • 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS) Bangalore, India 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS) IEEE , (2014). 978-1-4799-3635-9 Discovering signature of social networks with application to community detection , (2014). 1 7 6734901 , 10.1109/COMSNETS.2014.6734901 http://ieeexplore.ieee.org/document/6734901/
  • , Trust Prediction via Belief Propagation, ACM Transactions on Information Systems, 32, 3, (1), (2014).
  • , A generalized theory of preferential linking, Physica A: Statistical Mechanics and its Applications, 10.1016/j.physa.2014.08.026, 415, (544-556), (2014).
  • , RETRACTED: A Clustering-based Link Prediction Method in Social Networks, Procedia Computer Science, 29, (432), (2014).
  • , Degree-corrected stochastic block models and reliability in networks, Physica A: Statistical Mechanics and its Applications, 393, (553), (2014).
  • 2014 IEEE International Conference on Big Data (Big Data) Washington, DC, USA 2014 IEEE International Conference on Big Data (Big Data) IEEE , (2014). 978-1-4799-5666-1 HBGSim: A structural similarity measurement over heterogeneous big graphs , (2014). 31 38 7004465 , 10.1109/BigData.2014.7004465 http://ieeexplore.ieee.org/document/7004465/ 2014 IEEE Joint Intelligence and Security Informatics Conference (JISIC) The Hague, Netherlands 2014 IEEE Joint Intelligence and Security Informatics Conference IEEE , (2014). 978-1-4799-6364-5 978-1-4799-6363-8 Predicting Links in Multi-relational Networks , (2014). 107 114 6975561 , 10.1109/JISIC.2014.25 http://ieeexplore.ieee.org/document/6975561/ the 23rd ACM International Conference CIKM '14 Shanghai, China Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14 Conference on Information and Knowledge Management ACM Press New York, New York, USA , (2014). 9781450325981 , 10.1145/2661829 20141107100516 http://dl.acm.org/citation.cfm?doid=2661829 Predictability of Distrust with Interaction Data , (2014). 181 190 , 10.1145/2661829.2661988 20141107100517 http://dl.acm.org/citation.cfm?doid=2661829.2661988
  • , Semantic Breakthrough in Drug Discovery, Synthesis Lectures on the Semantic Web: Theory and Technology, 4, 2, (1), (2014).
  • , Evolutionary Network Analysis, ACM Computing Surveys, 47, 1, (1), (2014).
  • 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) China 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) IEEE , (2014). 978-1-4799-5877-1 SDHM: A hybrid model for spammer detection in Weibo , (2014). 942 947 6921699 , 10.1109/ASONAM.2014.6921699 http://ieeexplore.ieee.org/document/6921699/
  • , Predictive Analytics of Social Networks, Emerging Methods in Predictive Analytics, 10.4018/978-1-4666-5063-3.ch013, (297-333)
  • , A unified community detection algorithm in complex network, Neurocomputing, 130, (36), (2014).
  • the 23rd ACM International Conference CIKM '14 Shanghai, China Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14 Conference on Information and Knowledge Management ACM Press New York, New York, USA , (2014). 9781450325981 , 10.1145/2661829 20141107100516 http://dl.acm.org/citation.cfm?doid=2661829 Relationship Emergence Prediction in Heterogeneous Networks through Dynamic Frequent Subgraph Mining , (2014). 1649 1658 , 10.1145/2661829.2661916 20141107100519 http://dl.acm.org/citation.cfm?doid=2661829.2661916
  • , Predicting missing links via significant paths, EPL (Europhysics Letters), 106, 1, (18008), (2014).
  • , A Network-based Recommendation Algorithm via Improved Similarity Model, Information Technology Journal, 13, 5, (978), (2014).
  • , Searching for people to follow in social networks, Expert Systems with Applications, 41, 16, (7455), (2014).
  • , Semisupervised Classification Through the Bag-of-Paths Group Betweenness, IEEE Transactions on Neural Networks and Learning Systems, 25, 6, (1173), (2014).
  • , STUN: querying spatio-temporal uncertain (social) networks, Social Network Analysis and Mining, 4, 1, (2014).
  • , Diversifying Citation Recommendations, ACM Transactions on Intelligent Systems and Technology, 5, 4, (1), (2014).
  • 2014 IEEE International Conference on Data Mining Workshop (ICDMW) Shenzhen, China 2014 IEEE International Conference on Data Mining Workshop IEEE , (2014). 978-1-4799-4274-9 978-1-4799-4275-6 A Link Prediction Method That Can Learn from Network Dynamics , (2014). 549 553 7022644 , 10.1109/ICDMW.2014.12 http://ieeexplore.ieee.org/document/7022644/ 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) Warsaw, Poland 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) IEEE , (2014). 978-1-4799-4143-8 A Link Prediction Approach for Item Recommendation with Complex Number , (2014). 205 212 6927544 , 10.1109/WI-IAT.2014.35 http://ieeexplore.ieee.org/document/6927544/ 2014 IEEE International Conference on Orange Technologies (ICOT) Xi'an, China 2014 International Conference on Orange Technologies IEEE , (2014). 978-1-4799-6284-6 A visualization toolkit for online social network propagation and influence analysis with content features , (2014). 129 132 6956616 , 10.1109/ICOT.2014.6956616 http://ieeexplore.ieee.org/document/6956616/
  • , An Experimental Comparison of the Link Prediction Techniques in Social Networks, International Journal of Modeling and Optimization, 4, 1, (21), (2014).
  • , Entity Resolution on Multiple Relations, Innovative Techniques and Applications of Entity Resolution, 10.4018/978-1-4666-5198-2.ch006, (123-139)
  • , Similarity from Multi-Dimensional Scaling: Solving the Accuracy and Diversity Dilemma in Information Filtering, PLoS ONE, 9, 10, (e111005), (2014).
  • , Evaluating performance of link prediction in scale-free evolving networks and a Facebook community, Social Network Analysis and Mining, 4, 1, (2014).
  • , Dynamic Stochastic Blockmodels for Time-Evolving Social Networks, IEEE Journal of Selected Topics in Signal Processing, 8, 4, (552), (2014).
  • 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC San Diego, CA, USA 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) IEEE , (2014). 978-1-4799-3840-7 Extracting deep social relationships from photos , (2014). 453 456 6973949 , 10.1109/SMC.2014.6973949 http://ieeexplore.ieee.org/document/6973949/
  • , Mining hidden links in social networks to achieve equilibrium, Theoretical Computer Science, 556, (13), (2014).
  • , Predicting missing links via effective paths, Physica A: Statistical Mechanics and its Applications, 413, (515), (2014).
  • , Knowledge Discovery and Data Mining, Computing Handbook, Third Edition, 10.1201/b16768-23, (19-1-19-22), (2014).
  • 2014 International Conference on Management of e-Commerce and e-Government (ICMeCG) Shanghai, China 2014 International Conference on Management of e-Commerce and e-Government IEEE , (2014). 978-1-4799-6543-4 A Review of Link Prediction in Social Networks , (2014). 147 150 7046907 , 10.1109/ICMeCG.2014.38 http://ieeexplore.ieee.org/document/7046907/ 2014 11th Web Information System and Application Conference (WISA) Tianjin, China 2014 11th Web Information System and Application Conference IEEE , (2014). 978-1-4799-5726-2 978-1-4799-5727-9 A New Method for Link Prediction Using Various Features in Social Networks , (2014). 144 147 7058003 , 10.1109/WISA.2014.34 http://ieeexplore.ieee.org/document/7058003/ 2014 IEEE 38th International Computer Software and Applications Conference Workshops (COMPSACW) Vasteras, Sweden 2014 IEEE 38th International Computer Software and Applications Conference Workshops IEEE , (2014). 978-1-4799-3578-9 Cross-Domain Scientific Collaborations Prediction with Citation Information , (2014). 229 233 6903134 , 10.1109/COMPSACW.2014.127 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6903134 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI) Budapest 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI) IEEE , (2014). 978-1-4799-5338-7 Link prediction in weighted symptom networks , (2014). 273 278 7028688 , 10.1109/CINTI.2014.7028688 http://ieeexplore.ieee.org/document/7028688/ 2014 IEEE International Conference on Big Data (Big Data) Washington, DC, USA 2014 IEEE International Conference on Big Data (Big Data) IEEE , (2014). 978-1-4799-5666-1 Temporal bipartite projection and link prediction for online social networks , (2014). 52 59 7004444 , 10.1109/BigData.2014.7004444 http://ieeexplore.ieee.org/document/7004444/ the 14th International Conference i-KNOW '14 Graz, Austria Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business - i-KNOW '14 Knowledge Technologies and Data-driven Business ACM Press New York, New York, USA , (2014). 9781450327695 , 10.1145/2637748 20141107100516 http://dl.acm.org/citation.cfm?doid=2637748 Predicting the stability of user interaction ties in Twitter , (2014). 1 8 , 10.1145/2637748.2638428 20141107100516 http://dl.acm.org/citation.cfm?doid=2637748.2638428 2014 International Conference on Behavior, Economic and Social Computing (BESC) Shanghai, China 2014 International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2014) IEEE , (2014). 978-1-4799-6980-7 Friend recommendation of microblog in classification framework: Using multiple social behavior features , (2014). 1 6 7059527 , 10.1109/BESC.2014.7059527 http://ieeexplore.ieee.org/document/7059527/
  • , Personalized recommendation with corrected similarity, Journal of Statistical Mechanics: Theory and Experiment, 2014, 7, (P07004), (2014).
  • , A Unified Framework of Latent Feature Learning in Social Media, IEEE Transactions on Multimedia, 16, 6, (1624), (2014).
  • , Prediction of disease genes using tissue-specified gene-gene network, BMC Systems Biology, 8, Suppl 3, (S3), (2014).
  • , A Survey of Social-Aware Routing Protocols in Delay Tolerant Networks: Applications, Taxonomy and Design-Related Issues, IEEE Communications Surveys & Tutorials, 16, 1, (556), (2014).
  • , Remodeling the network for microgroup detection on microblog, Knowledge and Information Systems, 39, 3, (643), (2014).
  • the 23rd ACM International Conference CIKM '14 Shanghai, China Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14 Conference on Information and Knowledge Management ACM Press New York, New York, USA , (2014). 9781450325981 , 10.1145/2661829 20141107100516 http://dl.acm.org/citation.cfm?doid=2661829 High Impact Academic Paper Prediction Using Temporal and Topological Features , (2014). 491 498 , 10.1145/2661829.2662066 20141107100517 http://dl.acm.org/citation.cfm?doid=2661829.2662066
  • , Improving robustness of complex networks via the effective graph resistance, The European Physical Journal B, 87, 9, (2014).
  • , Compare two community-based personalized information recommendation algorithms, Physica A: Statistical Mechanics and its Applications, 398, (199), (2014).
  • 2014 IEEE International Conference on Healthcare Informatics (ICHI) Verona, Italy 2014 IEEE International Conference on Healthcare Informatics IEEE , (2014). 978-1-4799-5701-9 Drug-Drug Interactions Detection from Online Heterogeneous Healthcare Networks , (2014). 7 16 7052464 , 10.1109/ICHI.2014.9 http://ieeexplore.ieee.org/document/7052464/ 2014 Brazilian Conference on Intelligent Systems (BRACIS) Sao Paulo, Brazil 2014 Brazilian Conference on Intelligent Systems IEEE , (2014). Semantic Unlink Prediction in Evolving Social Networks through Probabilistic Description Logic , (2014). 372 377 6984859 , 10.1109/BRACIS.2014.73 http://ieeexplore.ieee.org/document/6984859/
  • , Predicting and recommending collaborations: An author-, institution-, and country-level analysis, Journal of Informetrics, 10.1016/j.joi.2014.01.008, 8, 2, (295-309), (2014).
  • 2014 IEEE International Conference on Data Mining (ICDM) Shenzhen, China 2014 IEEE International Conference on Data Mining IEEE , (2014). 978-1-4799-4302-9 978-1-4799-4303-6 Mining Query-Based Subnetwork Outliers in Heterogeneous Information Networks , (2014). 1127 1132 7023458 , 10.1109/ICDM.2014.85 http://ieeexplore.ieee.org/document/7023458/ 2014 IEEE 17th International Conference on Computational Science and Engineering (CSE) Chengdu, China 2014 IEEE 17th International Conference on Computational Science and Engineering IEEE , (2014). 978-1-4799-7981-3 978-1-4799-7980-6 Dynamics Nature and Link Prediction Methods in Opportunistic Networks , (2014). 1362 1368 7023769 , 10.1109/CSE.2014.259 http://ieeexplore.ieee.org/document/7023769/
  • , Towards evaluating and enhancing the reach of online health forums for smoking cessation, Network Modeling Analysis in Health Informatics and Bioinformatics, 3, 1, (2014).
  • , Supervised link prediction in symptom networks with evolving case, Measurement, 56, (231), (2014).
  • , The Impact of Name Ambiguity on Properties of Coauthorship Networks, Journal of Information Science Theory and Practice, 10.1633/JISTaP.2014.2.2.1, 2, 2, (6-15), (2014).
  • , GA-TVRC-Het: genetic algorithm enhanced time varying relational classifier for evolving heterogeneous networks, Data Mining and Knowledge Discovery, 28, 3, (670), (2014).
  • , Friend or foe? Fake profile identification in online social networks, Social Network Analysis and Mining, 4, 1, (2014).
  • 2014 IEEE International Conference on Information Reuse and Integration (IRI) Redwood City, CA, USA Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) IEEE , (2014). 978-1-4799-5880-1 A proximity measure for link prediction in social user-item networks , (2014). 710 717 7051959 , 10.1109/IRI.2014.7051959 http://ieeexplore.ieee.org/document/7051959/ 2014 IEEE 30th International Conference on Data Engineering (ICDE) Chicago, IL, USA 2014 IEEE 30th International Conference on Data Engineering IEEE , (2014). 978-1-4799-2555-1 Fast top-k path-based relevance query on massive graphs , (2014). 316 327 6816661 , 10.1109/ICDE.2014.6816661 http://ieeexplore.ieee.org/document/6816661/ 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) China 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) IEEE , (2014). 978-1-4799-5877-1 A model of consistent node types in signed directed social networks , (2014). 72 80 6921562 , 10.1109/ASONAM.2014.6921562 http://ieeexplore.ieee.org/document/6921562/ 2014 IEEE International Conference on Data Mining (ICDM) Shenzhen, China 2014 IEEE International Conference on Data Mining IEEE , (2014). 978-1-4799-4302-9 978-1-4799-4303-6 LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data , (2014). 300 309 7023347 , 10.1109/ICDM.2014.22 http://ieeexplore.ieee.org/document/7023347/ 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) Warsaw, Poland 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) IEEE , (2014). 978-1-4799-4143-8 Link Prediction Based on Multi-steps Resource Allocation , (2014). 355 360 6927565 , 10.1109/WI-IAT.2014.56 http://ieeexplore.ieee.org/document/6927565/ 2013 International Computer Science and Engineering Conference (ICSEC) Nakorn Pathom, Thailand 2013 International Computer Science and Engineering Conference (ICSEC) IEEE , (2013). 978-1-4673-5324-3 978-1-4673-5322-9 Analysis model for measuring information flow in social networks , (2013). 348 353 6694807 , 10.1109/ICSEC.2013.6694807 http://ieeexplore.ieee.org/document/6694807/
  • , Exploring social tagging for personalized community recommendations, User Modeling and User-Adapted Interaction, 23, 2-3, (249), (2013).
  • , A Nash Equilibrium Based Algorithm for Mining Hidden Links in Social Networks, Combinatorial Optimization and Applications, 10.1007/978-3-319-03780-6_13, (141-152), (2013).
  • , Structure and dynamics of molecular networks: A novel paradigm of drug discovery, Pharmacology & Therapeutics, 10.1016/j.pharmthera.2013.01.016, 138, 3, (333-408), (2013).
  • , Exploiting behaviors of communities of twitter users for link prediction, Social Network Analysis and Mining, 3, 4, (1063), (2013).
  • 2013 9th International Conference on Natural Computation (ICNC) Shenyang, China 2013 Ninth International Conference on Natural Computation (ICNC) IEEE , (2013). 978-1-4673-4714-3 A prediction method enhanced by the degree of nodes , (2013). 1515 1519 6818221 , 10.1109/ICNC.2013.6818221 http://ieeexplore.ieee.org/document/6818221/ 2013 IEEE International Conference on High Performance Computing and Communications (HPCC) & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (EUC) Zhangjiajie, China 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing IEEE , (2013). 978-0-7695-5088-6 Random Walk Based Inverse Influence Research in Online Social Networks , (2013). 2206 2213 6832199 , 10.1109/HPCC.and.EUC.2013.316 http://ieeexplore.ieee.org/document/6832199/ 2013 Ninth International Conference on Computational Intelligence and Security (CIS) Emeishan 614201, China 2013 Ninth International Conference on Computational Intelligence and Security IEEE , (2013). 978-1-4799-2549-0 978-1-4799-2548-3 Random Walk with Pre-filtering for Social Link Prediction , (2013). 139 143 6746372 , 10.1109/CIS.2013.36 http://ieeexplore.ieee.org/document/6746372/
  • , Bayesian analysis for partially observed network data, missing ties, attributes and actors, Social Networks, 35, 4, (514), (2013).
  • , Predicting link directions via a recursive subgraph-based ranking, Physica A: Statistical Mechanics and its Applications, 392, 16, (3402), (2013).
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  • , Data mining of social networks represented as graphs, Computer Science Review, 7, (1), (2013).
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  • 2013 IEEE International Conference on High Performance Computing and Communications (HPCC) & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (EUC) Zhangjiajie, China 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing IEEE , (2013). 978-0-7695-5088-6 Web Service QoS Prediction under Sparse Data via Local Link Prediction , (2013). 2285 2290 6832211 , 10.1109/HPCC.and.EUC.2013.328 http://ieeexplore.ieee.org/document/6832211/ 2013 IEEE Seventh International Conference on Research Challenges in Information Science (RCIS) Paris, France IEEE 7th International Conference on Research Challenges in Information Science (RCIS) IEEE , (2013). 978-1-4673-2914-9 978-1-4673-2912-5 Towards a hybrid algorithm for extracting maximal frequent conceptual links in social networks , (2013). 1 8 6577712 , 10.1109/RCIS.2013.6577712 http://ieeexplore.ieee.org/document/6577712/
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  • 2013 IEEE International Conference on Green Computing and Communications (GreenCom) and IEEE Internet of Things(iThings) and IEEE Cyber, Physical and Social Computing(CPSCom) Beijing, China 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing IEEE , (2013). 978-0-7695-5046-6 Friend Recommendation Based on the Similarity of Micro-blog User Model , (2013). 2200 2204 6682425 , 10.1109/GreenCom-iThings-CPSCom.2013.415 http://ieeexplore.ieee.org/document/6682425/ 2013 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) Bangkok, Thailand 2013 IEEE International Conference on Industrial Engineering and Engineering Management IEEE , (2013). 978-1-4799-0986-5 Measuring the performance of viral marketing based on the dynamic behavior of social networks , (2013). 432 436 6962448 , 10.1109/IEEM.2013.6962448 http://ieeexplore.ieee.org/document/6962448/ 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC) Shenyang, China 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control IEEE , (2013). 978-0-7695-5122-7 Recommending Items via Interest-Similar Cluster Identification in Online Social Networks , (2013). 331 334 6840465 , 10.1109/IMCCC.2013.76 http://ieeexplore.ieee.org/document/6840465/
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  • , Folksonomy link prediction based on a tripartite graph for tag recommendation, Journal of Intelligent Information Systems, 40, 2, (307), (2013).
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  • 2013 IEEE International Conference on High Performance Computing and Communications (HPCC) & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (EUC) Zhangjiajie, China 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing IEEE , (2013). 978-0-7695-5088-6 Friendship Prediction Based on the Fusion of Topology and Geographical Features in LBSN , (2013). 2224 2230 6832202 , 10.1109/HPCC.and.EUC.2013.319 http://ieeexplore.ieee.org/document/6832202/
  • , Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses, PLoS ONE, 8, 5, (e58977), (2013).
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  • , Effectiveness of Link Prediction for Face-to-Face Behavioral Networks, PLoS ONE, 8, 12, (e81727), (2013).
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  • 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) Atlanta, GA, USA 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) IEEE , (2013). 978-0-7695-5145-6 978-1-4799-2902-3 An Empirical Study of User Behaviors on Pinterest Social Network , (2013). 402 409 6690043 , 10.1109/WI-IAT.2013.57 http://ieeexplore.ieee.org/document/6690043/
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  • , From Frequent Features to Frequent Social Links, International Journal of Information System Modeling and Design, 10.4018/jismd.2013070104, 4, 3, (76-98), (2013).
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  • , Mining dynamic social networks from public news articles for company value prediction, Social Network Analysis and Mining, 2, 3, (217), (2012).
  • 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) Santander, Spain 2012 IEEE International Workshop on Machine Learning for Signal Processing IEEE , (2012). 978-1-4673-1026-0 978-1-4673-1024-6 978-1-4673-1025-3 Link prediction in weighted networks , (2012). 1 6 6349745 , 10.1109/MLSP.2012.6349745 http://ieeexplore.ieee.org/document/6349745/
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  • 2012 International Conference on Privacy, Security, Risk and Trust (PASSAT) Amsterdam 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing IEEE , (2012). 978-1-4673-5638-1 978-0-7695-4848-7 Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data , (2012). 969 974 6406354 , 10.1109/SocialCom-PASSAT.2012.102 http://ieeexplore.ieee.org/document/6406354/
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  • 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane) Brisbane, Australia The 2012 International Joint Conference on Neural Networks (IJCNN) IEEE , (2012). 978-1-4673-1490-9 978-1-4673-1488-6 978-1-4673-1489-3 Predicting missing links in social networks with hierarchical dirichlet processes , (2012). 1 8 6252619 , 10.1109/IJCNN.2012.6252619 http://ieeexplore.ieee.org/document/6252619/
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  • 2012 IEEE Thirteenth International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM) San Francisco, CA, USA 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) IEEE , (2012). 978-1-4673-1239-4 978-1-4673-1238-7 978-1-4673-1237-0 Improving link prediction in intermittently connected wireless networks by considering link and proximity stabilities , (2012). 1 10 6263701 , 10.1109/WoWMoM.2012.6263701 http://ieeexplore.ieee.org/document/6263701/
  • , A group trust metric for identifying people of trust in online social networks, Expert Systems with Applications, 39, 18, (13173), (2012).
  • , A Link-Based Cluster Ensemble Approach for Categorical Data Clustering, IEEE Transactions on Knowledge and Data Engineering, 24, 3, (413), (2012).
  • 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC) Austin, TX, USA 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC) IEEE , (2012). 978-1-4673-4883-6 978-1-4673-4881-2 978-1-4673-4882-9 Evolution of disconnected components in social networks: Patterns and a generative model , (2012). 305 313 6407772 , 10.1109/PCCC.2012.6407772 http://ieeexplore.ieee.org/document/6407772/
  • , Object Ranking in Evolutional Networks via Link Prediction, Transactions of the Japanese Society for Artificial Intelligence, 27, 3, (223), (2012).
  • , Latent Feature Kernels for Link Prediction on Sparse Graphs, IEEE Transactions on Neural Networks and Learning Systems, 23, 11, (1793), (2012).
  • 2012 International Conference on Privacy, Security, Risk and Trust (PASSAT) Amsterdam, Netherlands 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing IEEE , (2012). 978-1-4673-5638-1 978-0-7695-4848-7 How Are You Related? Predicting the Type of a Social Relationship Using Call Graph Data , (2012). 111 116 6406275 , 10.1109/SocialCom-PASSAT.2012.79 http://ieeexplore.ieee.org/document/6406275/
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  • 2012 International Conference on Social Informatics (SocialInformatics) Alexandria, VA, USA 2012 International Conference on Social Informatics IEEE , (2012). 978-1-4799-0234-7 978-0-7695-5015-2 Network Reconstruction under Compressive Sensing , (2012). 19 25 6542417 , 10.1109/SocialInformatics.2012.84 http://ieeexplore.ieee.org/document/6542417/ 2012 International Conference on Social Informatics (SocialInformatics) Alexandria, VA, USA 2012 International Conference on Social Informatics IEEE , (2012). 978-1-4799-0234-7 978-0-7695-5015-2 Ranking Given Names: Algorithms and Evaluation Paradigms , (2012). 185 191 6542439 , 10.1109/SocialInformatics.2012.65 http://ieeexplore.ieee.org/document/6542439/
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  • 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane) Brisbane, Australia The 2012 International Joint Conference on Neural Networks (IJCNN) IEEE , (2012). 978-1-4673-1490-9 978-1-4673-1488-6 978-1-4673-1489-3 Time Series Based Link Prediction , (2012). 1 7 6252471 , 10.1109/IJCNN.2012.6252471 http://ieeexplore.ieee.org/document/6252471/
  • , Network-Based Modeling in Epidemiology, International Journal of Information System Modeling and Design, 10.4018/jismd.2012070103, 3, 3, (46-65), (2012).
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  • , Social Network Inspired Approach to Intelligent Monitoring of Intelligence Data, Social Network Mining, Analysis, and Research Trends, 10.4018/978-1-61350-513-7.ch006, (79-100), (2012).
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  • , Detecting community structure in networks using edge prediction methods, Journal of Statistical Mechanics: Theory and Experiment, 2012, 09, (P09008), (2012).
  • 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) Istanbul 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining IEEE , (2012). 978-1-4673-2497-7 978-0-7695-4799-2 Fast Recommendation on Bibliographic Networks , (2012). 480 487 6425721 , 10.1109/ASONAM.2012.82 http://ieeexplore.ieee.org/document/6425721/ 2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC 2012) Limassol, Cyprus 2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC) IEEE , (2012). 978-1-4577-1379-8 978-1-4577-1378-1 978-1-4577-1377-4 Tracking topology dynamicity for link prediction in intermittently connected wireless networks , (2012). 469 474 6314249 , 10.1109/IWCMC.2012.6314249 http://ieeexplore.ieee.org/document/6314249/
  • , Removing spurious interactions in complex networks, Physical Review E, 85, 3, (2012).
  • 2012 Sixth International Conference on Research Challenges in Information Science (RCIS) Valencia, Spain 2012 Sixth International Conference on Research Challenges in Information Science (RCIS) IEEE , (2012). 978-1-4577-1938-7 978-1-4577-1936-3 978-1-4577-1937-0 How to extract frequent links with frequent itemsets in social networks? , (2012). 1 10 6240432 , 10.1109/RCIS.2012.6240432 http://ieeexplore.ieee.org/document/6240432/ 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems (CCIS) Hangzhou, China 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems IEEE , (2012). 978-1-4673-1857-0 978-1-4673-1855-6 Link prediction on evolving network using tensor-based node similarity , (2012). 154 158 6664387 , 10.1109/CCIS.2012.6664387 http://ieeexplore.ieee.org/document/6664387/ 2012 International Conference on Privacy, Security, Risk and Trust (PASSAT) Amsterdam, Netherlands 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing IEEE , (2012). 978-1-4673-5638-1 978-0-7695-4848-7 Inferring Realistic Intra-hospital Contact Networks Using Link Prediction and Computer Logins , (2012). 572 578 6406310 , 10.1109/SocialCom-PASSAT.2012.113 http://ieeexplore.ieee.org/document/6406310/ 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN) Sao Carlos, Brazil 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN) IEEE , (2012). 978-1-4673-4794-5 978-1-4673-4793-8 978-1-4673-4792-1 Structural link prediction using community information on Twitter , (2012). 132 137 6412391 , 10.1109/CASoN.2012.6412391 http://ieeexplore.ieee.org/document/6412391/
  • , A Framework for Fostering Multidisciplinary Research Collaboration and Scientific Networking within University Environs, Knowledge Management Handbook, 10.1201/b12285-13, (207-217), (2012).
  • 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC Seoul, Korea (South) 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) IEEE , (2012). 978-1-4673-1714-6 978-1-4673-1713-9 978-1-4673-1712-2 Features for link prediction in social networks: A comprehensive study , (2012). 1706 1711 6377983 , 10.1109/ICSMC.2012.6377983 http://ieeexplore.ieee.org/document/6377983/ 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Monticello, IL, USA 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton) IEEE , (2012). 978-1-4673-4539-2 978-1-4673-4537-8 978-1-4673-4538-5 Similarity-based network formation , (2012). 394 399 6483245 , 10.1109/Allerton.2012.6483245 http://ieeexplore.ieee.org/document/6483245/
  • , A graph-based action network framework to identify prestigious members through member's prestige evolution, Decision Support Systems, 53, 1, (44), (2012).
  • , Predicting missing contacts in mobile social networks, Pervasive and Mobile Computing, 8, 5, (698), (2012).
  • 2012 International Conference on Privacy, Security, Risk and Trust (PASSAT) Amsterdam, Netherlands 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing IEEE , (2012). 978-1-4673-5638-1 978-0-7695-4848-7 Predicting the Evolution of Social Networks: Optimal Time Window Size for Increased Accuracy , (2012). 21 30 6406266 , 10.1109/SocialCom-PASSAT.2012.11 http://ieeexplore.ieee.org/document/6406266/
  • , Network Similarity Decomposition (NSD): A Fast and Scalable Approach to Network Alignment, IEEE Transactions on Knowledge and Data Engineering, 24, 12, (2232), (2012).
  • 2012 3rd International Workshop on Cognitive Information Processing (CIP) Baiona 2012 3rd International Workshop on Cognitive Information Processing (CIP) IEEE , (2012). 978-1-4673-1877-8 978-1-4673-1878-5 Detecting hierarchical structure in networks , (2012). 1 6 6232913 , 10.1109/CIP.2012.6232913 http://ieeexplore.ieee.org/document/6232913/
  • , Topic Diffusion Behavior Tracking in Online Social Network, Communications and Information Processing, 10.1007/978-3-642-31968-6_86, (725-733), (2012).
  • IEEE INFOCOM 2012 - IEEE Conference on Computer Communications Orlando, FL, USA 2012 Proceedings IEEE INFOCOM IEEE , (2012). 978-1-4673-0775-8 978-1-4673-0773-4 978-1-4673-0774-1 Cosine-neighbourhood-refinement: Towards a robust network formation mechanism , (2012). 1710 1718 6195542 , 10.1109/INFCOM.2012.6195542 http://ieeexplore.ieee.org/document/6195542/ 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT 2012) Coimbatore 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12) IEEE , (2012). Countering terrorism through dark web analysis , (2012). 1 5 6396055 , 10.1109/ICCCNT.2012.6396055 http://ieeexplore.ieee.org/document/6396055/
  • , Significant Communities in Large Sparse Networks, PLoS ONE, 7, 3, (e33721), (2012).
  • , Bistability through Triadic Closure, Internet Mathematics, 8, 4, (402), (2012).
  • 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) Istanbul 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining IEEE , (2012). 978-1-4673-2497-7 978-0-7695-4799-2 A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks , (2012). 996 1001 6425629 , 10.1109/ASONAM.2012.173 http://ieeexplore.ieee.org/document/6425629/ 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) Istanbul 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining IEEE , (2012). 978-1-4673-2497-7 978-0-7695-4799-2 Where's the Money? The Social Behavior of Investors in Facebook's Small World , (2012). 158 162 6425769 , 10.1109/ASONAM.2012.36 http://ieeexplore.ieee.org/document/6425769/ 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) Istanbul 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining IEEE , (2012). 978-1-4673-2497-7 978-0-7695-4799-2 Predicting Friends and Foes in Signed Networks Using Inductive Inference and Social Balance Theory , (2012). 384 388 6425735 , 10.1109/ASONAM.2012.69 http://ieeexplore.ieee.org/document/6425735/
  • , Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm, Physica A: Statistical Mechanics and its Applications, 391, 22, (5704), (2012).
  • , Fuzzy Orders-of-Magnitude-Based Link Analysis for Qualitative Alias Detection, IEEE Transactions on Knowledge and Data Engineering, 10.1109/TKDE.2010.255, 24, 4, (649-664), (2012).
  • , The tipping point: F-score as a function of the number of retrieved items, Information Processing & Management, 48, 6, (1171), (2012).
  • , Leveraging personal photos to inferring friendships in social network services, Expert Systems with Applications, 39, 8, (6955), (2012).
  • , A boosted-trees method for name disambiguation, Scientometrics, 93, 2, (391), (2012).
  • 2012 IEEE International Conference on Data Engineering Workshops (ICDEW) Arlington, VA, USA 2012 IEEE 28th International Conference on Data Engineering Workshops IEEE , (2012). 978-0-7695-4748-0 978-1-4673-1640-8 Finding Skyline Nodes in Large Networks , (2012). 198 204 6313680 , 10.1109/ICDEW.2012.53 http://ieeexplore.ieee.org/document/6313680/
  • , Relationship strength estimation for online social networks with the study on Facebook, Neurocomputing, 95, (89), (2012).
  • , Social Network Construction in the Information Age, Social Network Mining, Analysis, and Research Trends, 10.4018/978-1-61350-513-7.ch009, (131-155), (2012).
  • 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Liverpool 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications IEEE , (2012). 978-1-4673-2172-3 978-0-7695-4745-9 The Multi-layer Imbrication for Data Leakage Prevention from Mobile Devices , (2012). 813 819 6296053 , 10.1109/TrustCom.2012.274 http://ieeexplore.ieee.org/document/6296053/ 2012 IEEE International Conference on Data Engineering (ICDE 2012) Arlington, VA, USA 2012 IEEE 28th International Conference on Data Engineering IEEE , (2012). 978-0-7695-4747-3 978-1-4673-0042-1 Ego-centric Graph Pattern Census , (2012). 234 245 6228087 , 10.1109/ICDE.2012.113 http://ieeexplore.ieee.org/document/6228087/
  • , Biomine: A Network-Structured Resource of Biological Entities for Link Prediction, Bisociative Knowledge Discovery, 10.1007/978-3-642-31830-6_26, (364-378), (2012).
  • , Dynamics and Evolutional Patterns of Social Networks, Social Network Mining, Analysis, and Research Trends, 10.4018/978-1-61350-513-7.ch010, (156-170), (2012).
  • 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) Istanbul 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining IEEE , (2012). 978-1-4673-2497-7 978-0-7695-4799-2 Classification Analysis in Complex Online Social Networks Using Semantic Web Technologies , (2012). 1032 1039 6425623 , 10.1109/ASONAM.2012.179 http://ieeexplore.ieee.org/document/6425623/ 2012 9th Web Information Systems and Applications Conference (WISA) Haikou, China 2012 Ninth Web Information Systems and Applications Conference IEEE , (2012). 978-1-4673-3054-1 Using Non-topological Node Attributes to Improve Results of Link Prediction in Social Networks , (2012). 141 146 6385200 , 10.1109/WISA.2012.21 http://ieeexplore.ieee.org/document/6385200/
  • , Assessing Drug Target Association Using Semantic Linked Data, PLoS Computational Biology, 8, 7, (e1002574), (2012).
  • 2011 IEEE Congress on Evolutionary Computation (CEC) New Orleans, LA, USA 2011 IEEE Congress of Evolutionary Computation (CEC) IEEE , (2011). 978-1-4244-7834-7 Friend recommendations in social networks using genetic algorithms and network topology , (2011). 2207 2214 5949888 , 10.1109/CEC.2011.5949888 http://ieeexplore.ieee.org/document/5949888/ 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011) Kaohsiung City, Taiwan 2011 International Conference on Advances in Social Networks Analysis and Mining IEEE , (2011). 978-1-61284-758-0 SNAP: Towards a Validation of the Social Network Assembly Pipeline , (2011). 228 235 5992607 , 10.1109/ASONAM.2011.88 http://ieeexplore.ieee.org/document/5992607/ 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust (PASSAT) / 2011 IEEE Third Int'l Conference on Social Computing (SocialCom) Boston, MA, USA 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing IEEE , (2011). 978-1-4577-1931-8 978-0-7695-4578-3 Predicting Reciprocity in Social Networks , (2011). 49 56 6113094 , 10.1109/PASSAT/SocialCom.2011.110 http://ieeexplore.ieee.org/document/6113094/ 2011 IEEE International Conference on Granular Computing (GrC-2011) Kaohsiung, Taiwan 2011 IEEE International Conference on Granular Computing IEEE , (2011). 978-1-4577-0371-3 978-1-4577-0372-0 There exist correlations between editing behaviors and hyperlinks structure in Wikipedia , (2011). 790 795 6122699 , 10.1109/GRC.2011.6122699 http://ieeexplore.ieee.org/document/6122699/ 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust (PASSAT) / 2011 IEEE Third Int'l Conference on Social Computing (SocialCom) Boston, MA, USA 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing IEEE , (2011). 978-1-4577-1931-8 978-0-7695-4578-3 Link Prediction in Social Networks Using Computationally Efficient Topological Features , (2011). 73 80 6113097 , 10.1109/PASSAT/SocialCom.2011.20 http://ieeexplore.ieee.org/document/6113097/
  • , Link prediction based on a semi-local similarity index, Chinese Physics B, 20, 12, (128902), (2011).
  • , Analysis of user keyword similarity in online social networks, Social Network Analysis and Mining, 1, 3, (143), (2011).
  • 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011) Kaohsiung City, Taiwan 2011 International Conference on Advances in Social Networks Analysis and Mining IEEE , (2011). 978-1-61284-758-0 Content-based Modeling and Prediction of Information Dissemination , (2011). 21 28 5992581 , 10.1109/ASONAM.2011.61 http://ieeexplore.ieee.org/document/5992581/ 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011) Kaohsiung City, Taiwan 2011 International Conference on Advances in Social Networks Analysis and Mining IEEE , (2011). 978-1-61284-758-0 Applying Link Prediction to Ranking Candidates for High-Level Government Post , (2011). 145 152 5992574 , 10.1109/ASONAM.2011.54 http://ieeexplore.ieee.org/document/5992574/
  • , Link prediction in complex networks: A local naïve Bayes model, EPL (Europhysics Letters), 96, 4, (48007), (2011).
  • , Good versus optimal: Why network analytic methods need more systematic evaluation, Open Computer Science, 1, 1, (2011).
  • 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011) Kaohsiung City, Taiwan 2011 International Conference on Advances in Social Networks Analysis and Mining IEEE , (2011). 978-1-61284-758-0 A Framework of Recommendation System Based on Both Network Structure and Messages , (2011). 709 714 5992686 , 10.1109/ASONAM.2011.68 http://ieeexplore.ieee.org/document/5992686/
  • , Influence, originality and similarity in directed acyclic graphs, EPL (Europhysics Letters), 96, 1, (18004), (2011).
  • , Semi-supervised classification and betweenness computation on large, sparse, directed graphs, Pattern Recognition, 44, 6, (1212), (2011).
  • , Finding missing edges and communities in incomplete networks, Journal of Physics A: Mathematical and Theoretical, 44, 49, (495102), (2011).
  • 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) Beijing, China 2011 IEEE International Workshop on Machine Learning for Signal Processing IEEE , (2011). 978-1-4577-1621-8 Non-parametric co-clustering of large scale sparse bipartite networks on the GPU , (2011). 1 6 6064611 , 10.1109/MLSP.2011.6064611 http://ieeexplore.ieee.org/document/6064611/
  • , Mining competitor relationships from online news: A network-based approach, Electronic Commerce Research and Applications, 10, 4, (418), (2011).
  • 2011 Annual SRII Global Conference (SRII) San Jose, CA, USA 2011 Annual SRII Global Conference IEEE , (2011). 978-1-61284-415-2 e-Catalunya: An e-Government Service for Virtual Communities of Practice , (2011). 707 715 5958151 , 10.1109/SRII.2011.77 http://ieeexplore.ieee.org/document/5958151/
  • , Asymmetric network connectivity using weighted harmonic averages, EPL (Europhysics Letters), 93, 4, (40002), (2011).
  • , Dynamic Network Motifs: Evolutionary Patterns of Substructures in Complex Networks, Web Technologies and Applications, 10.1007/978-3-642-20291-9_33, (321-326), (2011).
  • , Parameterized centrality metric for network analysis, Physical Review E, 83, 6, (2011).
  • , Information filtering via preferential diffusion, Physical Review E, 83, 6, (2011).
  • IEEE INFOCOM 2011 - IEEE Conference on Computer Communications Shanghai, China 2011 Proceedings IEEE INFOCOM IEEE , (2011). 978-1-4244-9919-9 Secure friend discovery in mobile social networks , (2011). 1647 1655 5934958 , 10.1109/INFCOM.2011.5934958 http://ieeexplore.ieee.org/document/5934958/ 2011 International Conference on Internet Technology and Applications (iTAP) Wuhan, China 2011 International Conference on Internet Technology and Applications IEEE , (2011). 978-1-4244-7253-6 Weighted MUSE for Frequent Sub-Graph Pattern Finding in Uncertain DBLP Data , (2011). 1 6 6006415 , 10.1109/ITAP.2011.6006415 http://ieeexplore.ieee.org/document/6006415/
  • , Predictors of short-term decay of cell phone contacts in a large scale communication network, Social Networks, 33, 4, (245), (2011).
  • , FUZZY QUALITATIVE LINK ANALYSIS FOR ACADEMIC PERFORMANCE EVALUATION, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10.1142/S0218488511007131, 19, 03, (559-585), (2011).
  • 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011) Kaohsiung City, Taiwan 2011 International Conference on Advances in Social Networks Analysis and Mining IEEE , (2011). 978-1-61284-758-0 Co-author Relationship Prediction in Heterogeneous Bibliographic Networks , (2011). 121 128 5992571 , 10.1109/ASONAM.2011.112 http://ieeexplore.ieee.org/document/5992571/ 2011 IEEE International Conference on Multimedia and Expo (ICME) Barcelona, Spain 2011 IEEE International Conference on Multimedia and Expo IEEE , (2011). 978-1-61284-348-3 Security and privacy in online social networks: A survey , (2011). 1 6 6012166 , 10.1109/ICME.2011.6012166 http://ieeexplore.ieee.org/document/6012166/
  • , Spectral Regression with Low-Rank Approximation for Dynamic Graph Link Prediction, IEEE Intelligent Systems, 26, 4, (48), (2011).
  • 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Atlanta, GA, USA 2011 IEEE International Conference on Bioinformatics and Biomedicine IEEE , (2011). 978-1-4577-1799-4 Supervised Link Discovery on Large-Scale Biomedical Concept Networks , (2011). 562 568 6120502 , 10.1109/BIBM.2011.92 http://ieeexplore.ieee.org/document/6120502/
  • , A Link-Based Approach to the Cluster Ensemble Problem, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 12, (2396), (2011).
  • , Semantic linking through spaces for cyber-physical-socio intelligence: A methodology, Artificial Intelligence, 10.1016/j.artint.2010.09.009, 175, 5-6, (988-1019), (2011).
  • IEEE INFOCOM 2011 - IEEE Conference on Computer Communications Shanghai, China 2011 Proceedings IEEE INFOCOM IEEE , (2011). 978-1-4244-9919-9 “Who Are Your Friends?” — A Simple Mechanism that achieves perfect network formation , (2011). 566 570 5935227 , 10.1109/INFCOM.2011.5935227 http://ieeexplore.ieee.org/document/5935227/ Integration (IRI) Las Vegas, NV, USA 2011 IEEE International Conference on Information Reuse & Integration IEEE , (2011). 978-1-4577-0964-7 Network and profile based measures for user similarities on social networks , (2011). 292 298 6009562 , 10.1109/IRI.2011.6009562 http://ieeexplore.ieee.org/document/6009562/
  • , Predicting Adverse Drug Events Using Pharmacological Network Models, Science Translational Medicine, 3, 114, (114ra127), (2011).
  • , Ranking in collaboration networks using a group based metric, Journal of the Brazilian Computer Society, 17, 4, (255), (2011).
  • , Link prediction in complex networks: A survey, Physica A: Statistical Mechanics and its Applications, 390, 6, (1150), (2011).
  • 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) Lyon, France 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology IEEE , (2011). 978-1-4577-1373-6 Discovering the Evolutionary Patterns in Local Topology of an E-Mail Social Network , (2011). 102 105 6040816 , 10.1109/WI-IAT.2011.21 http://ieeexplore.ieee.org/document/6040816/
  • , Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes, Marketing Science, 30, 3, (513), (2011).
  • , Volume Removed - Publisher's Disclaimer, Energy Procedia, 10.1016/S1876-6102(14)00454-8, 13, (1-10380), (2011).
  • , Finding Relevant Papers Based on Citation Relations, Web-Age Information Management, 10.1007/978-3-642-23535-1_35, (403-414), (2011).
  • , DATA PRIVACY FOR SIMPLY ANONYMIZED SOCIAL NETWORK LOGS REPRESENTED AS GRAPHS – CONSIDERATIONS FOR GRAPH ALTERATION OPERATIONS, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 19, supp01, (107), (2011).
  • , Learning to Infer Social Ties in Large Networks, Machine Learning and Knowledge Discovery in Databases, 10.1007/978-3-642-23808-6_25, (381-397), (2011).
  • 2011 IEEE Fifth International Conference on Research Challenges in Information Science (RCIS) Gosier, France 2011 FIFTH INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN INFORMATION SCIENCE IEEE , (2011). 978-1-4244-8670-0 Social network analysis in epidemiology: Current trends and perspectives , (2011). 1 11 6006866 , 10.1109/RCIS.2011.6006866 http://ieeexplore.ieee.org/document/6006866/ 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011) Kaohsiung City, Taiwan 2011 International Conference on Advances in Social Networks Analysis and Mining IEEE , (2011). 978-1-61284-758-0 Link Prediction Based on Local Information , (2011). 382 386 5992628 , 10.1109/ASONAM.2011.43 http://ieeexplore.ieee.org/document/5992628/
  • , Densifying a behavioral recommender system by social networks link prediction methods, Social Network Analysis and Mining, 1, 3, (159), (2011).
  • , A relation context oriented approach to identify strong ties in social networks, Knowledge-Based Systems, 10.1016/j.knosys.2011.05.006, 24, 8, (1187-1195), (2011).
  • 2011 IEEE International Technology Management Conference (ITMC) San Jose, CA, USA First International Technology Management Conference IEEE , (2011). 978-1-61284-951-5 Finding business partners and building reciprocal relationships - A machine learning approach , (2011). 1069 1073 5996005 , 10.1109/ITMC.2011.5996005 http://ieeexplore.ieee.org/document/5996005/
  • , Link Prediction and Route Selection Based on Channel State Detection in UASNs, International Journal of Distributed Sensor Networks, 7, 1, (939864), (2011).
  • 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust (PASSAT) / 2011 IEEE Third Int'l Conference on Social Computing (SocialCom) Boston, MA, USA 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing IEEE , (2011). 978-1-4577-1931-8 978-0-7695-4578-3 Link Prediction Based on Subgraph Evolution in Dynamic Social Networks , (2011). 27 34 6113091 , 10.1109/PASSAT/SocialCom.2011.15 http://ieeexplore.ieee.org/document/6113091/ 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011) Kaohsiung City, Taiwan 2011 International Conference on Advances in Social Networks Analysis and Mining IEEE , (2011). 978-1-61284-758-0 The Dynamic Structural Patterns of Social Networks Based on Triad Transitions , (2011). 581 586 5992640 , 10.1109/ASONAM.2011.50 http://ieeexplore.ieee.org/document/5992640/
  • , Microgroup Mining on TSina via Network Structure and User Attribute, Advanced Data Mining and Applications, 10.1007/978-3-642-25856-5_11, (138-151), (2011).
  • 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011) Shanghai 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) IEEE , (2011). 978-1-61284-180-9 978-1-61284-181-6 Predicting missing links via local feature of common neighbors , (2011). 1038 1042 6019642 , 10.1109/FSKD.2011.6019642 http://ieeexplore.ieee.org/document/6019642/
  • , Link Prediction for Annotation Graphs Using Graph Summarization, The Semantic Web – ISWC 2011, 10.1007/978-3-642-25073-6_45, (714-729), (2011).
  • 2010 Sixth International Conference on Information Assurance and Security (IAS) Atlanta, GA, USA 2010 Sixth International Conference on Information Assurance and Security IEEE , (2010). 978-1-4244-7407-3 Link suggestions in terrorists networks using Semi Discrete Decomposition , (2010). 335 337 5604077 , 10.1109/ISIAS.2010.5604077 http://ieeexplore.ieee.org/document/5604077/
  • , Locality and attachedness‐based temporal social network growth dynamics analysis: A case study of evolving nanotechnology scientific collaboration networks, Journal of the American Society for Information Science and Technology, 61, 5, (964-977), (2010).
  • 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2010) Odense, Denmark 2010 International Conference on Advances in Social Networks Analysis and Mining IEEE , (2010). 978-1-4244-7787-6 Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks , (2010). 326 330 5562752 , 10.1109/ASONAM.2010.87 http://ieeexplore.ieee.org/document/5562752/
  • , Community Detection and Mining in Social Media, Synthesis Lectures on Data Mining and Knowledge Discovery, 2, 1, (1), (2010).
  • 2010 IEEE Second International Conference on Social Computing (SocialCom) Minneapolis, MN, USA 2010 IEEE Second International Conference on Social Computing IEEE , (2010). 978-1-4244-8439-3 Behavior Evolution and Event-Driven Growth Dynamics in Social Networks , (2010). 217 224 5590443 , 10.1109/SocialCom.2010.38 http://ieeexplore.ieee.org/document/5590443/
  • , EVIDENCE DIRECTED GENERATION OF PLAUSIBLE CRIME SCENARIOS WITH IDENTITY RESOLUTION, Applied Artificial Intelligence, 24, 4, (253), (2010).
  • , CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?, International Journal of Modern Physics C, 21, 10, (1217), (2010).
  • 2010 IEEE International Conference on Intelligence and Security Informatics Vancouver, BC, Canada 2010 IEEE International Conference on Intelligence and Security Informatics IEEE , (2010). 978-1-4244-6444-9 Developing a Dark Web collection and infrastructure for computational and social sciences , (2010). 59 64 5484774 , 10.1109/ISI.2010.5484774 http://ieeexplore.ieee.org/document/5484774/
  • , Empirical comparison of local structural similarity indices for collaborative-filtering-based recommender systems, Physics Procedia, 3, 5, (1887), (2010).
  • , Disclosing false identity through hybrid link analysis, Artificial Intelligence and Law, 10.1007/s10506-010-9085-9, 18, 1, (77-102), (2010).
  • , Link prediction based on local random walk, EPL (Europhysics Letters), 89, 5, (58007), (2010).
  • , SIMILARITY-BASED CLASSIFICATION IN PARTIALLY LABELED NETWORKS, International Journal of Modern Physics C, 21, 06, (813), (2010).
  • , Coauthorship networks and academic literature recommendation, Electronic Commerce Research and Applications, 9, 4, (323), (2010).
  • IEEE INFOCOM 2010 - IEEE Conference on Computer Communications San Diego, CA, USA 2010 Proceedings IEEE INFOCOM IEEE , (2010). 978-1-4244-5836-3 Know Thy Neighbor: Towards Optimal Mapping of Contacts to Social Graphs for DTN Routing , (2010). 1 9 5462135 , 10.1109/INFCOM.2010.5462135 http://ieeexplore.ieee.org/document/5462135/
  • , Collaborative filtering with diffusion-based similarity on tripartite graphs, Physica A: Statistical Mechanics and its Applications, 389, 6, (1259), (2010).
  • , Bridgeness: a local index on edge significance in maintaining global connectivity, Journal of Statistical Mechanics: Theory and Experiment, 2010, 10, (P10011), (2010).
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