graph_tool.inference.LatentLayerBaseState

class graph_tool.inference.LatentLayerBaseState

Bases: object

Base state for uncertain latent layer network inference.

Methods

collect_marginal([gs, total])	Collect marginal inferred network during MCMC runs.
collect_marginal_multigraph([gs])	Collect marginal latent multigraphs during MCMC runs.
get_ec([ew])	Return edge property map with layer membership.
mcmc_sweep([r, multiflip])	Perform sweeps of a Metropolis-Hastings acceptance-rejection sampling MCMC to sample network partitions and latent edges.
multiflip_mcmc_sweep(**kwargs)	Alias for mcmc_sweep() with multiflip=True.

collect_marginal(gs=None, total=False)

Collect marginal inferred network during MCMC runs.

Parameters:

glist of Graph (optional, default: None)

Previous marginal graphs.

Returns:

glist Graph

New list of marginal graphs, each with internal edge EdgePropertyMap "eprob", containing the marginal probabilities for each edge.

Notes

The posterior marginal probability of an edge $(i,j)$ is defined as

$${\pi }_{ij}=\sum _{\mathit{A}}{A}_{ij}P(\mathit{A}|\mathit{D})$$

where $P(\mathit{A}|\mathit{D})$ is the posterior probability given the data.

This function returns a list with the marginal graphs for every layer.

collect_marginal_multigraph(gs=None)

Collect marginal latent multigraphs during MCMC runs.

Parameters:

glist of Graph (optional, default: None)

Previous marginal multigraphs.

Returns:

glist of Graph

New marginal multigraphs, each with internal edge EdgePropertyMap "w" and "wcount", containing the edge multiplicities and their respective counts.

Notes

The mean posterior marginal multiplicity distribution of a multi-edge $(i,j)$ is defined as

$${\pi }_{ij}(w)=\sum _{\mathit{G}}{\delta }_{w,G_{ij}}P(\mathit{G}|\mathit{D})$$

where $P(\mathit{G}|\mathit{D})$ is the posterior probability of a multigraph $\mathit{G}$ given the data.

This function returns a list with the marginal graphs for every layer.

get_ec(ew=None)

Return edge property map with layer membership.

mcmc_sweep(r=0.5, multiflip=True, **kwargs)

Perform sweeps of a Metropolis-Hastings acceptance-rejection sampling MCMC to sample network partitions and latent edges. The parameter r controls the probability with which edge move will be attempted, instead of partition moves. The remaining keyword parameters will be passed to mcmc_sweep() or multiflip_mcmc_sweep(), if multiflip=True.

multiflip_mcmc_sweep(**kwargs)

Alias for mcmc_sweep() with multiflip=True.