Peer influence groups: identifying dense clusters in large networks (2001)
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| Venue: | SOCIAL NETWORKS |
| Citations: | 25 - 0 self |
BibTeX
@ARTICLE{Moody01peerinfluence,
author = {James Moody},
title = {Peer influence groups: identifying dense clusters in large networks},
journal = {SOCIAL NETWORKS},
year = {2001},
volume = {23},
pages = {261--283}
}
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Abstract
Sociologists have seen a dramatic increase in the size and availability of social network data. This represents a poverty of riches, however, since many of our analysis techniques cannot handle the resulting large (tens to hundreds of thousands of nodes) networks. In this paper, I provide a method for identifying dense regions within large networks based on a peer influence model. Using software familiar to most sociologists, the method reduces the network to a set of m position variables that can then be used in fast cluster analysis programs. The method is tested against simulated networks with a known small-world structure showing that the underlying clusters can be accurately recovered. I then compare the performance of the procedure with other subgroup detection algorithms on the MacRea and Gagnon prison friendship data and a larger adolescent friendship network, showing that the algorithm replicates other procedures for small networks and outperforms them on the
Keyphrases
large network peer influence group dense cluster position variable fast cluster analysis program social network data gagnon prison friendship data subgroup detection algorithm analysis technique adolescent friendship network peer influence model dense region dramatic increase known small-world structure small network
