graph_tool.inference.UncertainBlockState#

class graph_tool.inference.UncertainBlockState(g, q, q_default=0.0, aE=nan, nested=True, state_args={}, bstate=None, self_loops=False, **kwargs)[source]#

Bases: UncertainBaseState

Inference state of an uncertain graph, using the stochastic block model as a prior.

Parameters:

gGraph: Measured graph.
qEdgePropertyMap: Edge probabilities in range \([0,1]\).
q_defaultfloat (optional, default: 0.): Non-edge probability in range \([0,1]\).
aEfloat (optional, default: NaN): Expected total number of edges used in prior. If NaN, a flat prior will be used instead.
nestedboolean (optional, default: True): If True, a NestedBlockState will be used, otherwise BlockState.
state_argsdict (optional, default: {}): Arguments to be passed to NestedBlockState or BlockState.
bstateNestedBlockState or BlockState (optional, default: None): If passed, this will be used to initialize the block state directly.
self_loopsbool (optional, default: False): If True, it is assumed that the uncertain graph can contain self-loops.

References

[peixoto-reconstructing-2018]

Tiago P. Peixoto, “Reconstructing networks with unknown and heterogeneous errors”, Phys. Rev. X 8 041011 (2018). DOI: 10.1103/PhysRevX.8.041011 [sci-hub, @tor], arXiv: 1806.07956

Methods

`collect_marginal`([g])	Collect marginal inferred network during MCMC runs.
`collect_marginal_multigraph`([g])	Collect marginal latent multigraph during MCMC runs.
`copy`(**kwargs)	Return a copy of the state.
`entropy`([latent_edges, density])	Return the entropy, i.e. negative log-likelihood.
`get_block_state`()	Return the underlying block state, which can be either `BlockState` or `NestedBlockState`.
`get_edge_prob`(u, v[, entropy_args, epsilon])	Return conditional posterior log-probability of edge \((u,v)\).
`get_edges_prob`(elist[, entropy_args, epsilon])	Return conditional posterior log-probability of an edge list, with shape \((E,2)\).
`get_graph`()	Return the current inferred graph.
`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`.
`set_state`(g, w)
`virtual_add_edge`(u, v[, entropy_args])
`virtual_remove_edge`(u, v[, entropy_args])

collect_marginal(g=None)#

Collect marginal inferred network during MCMC runs.

Parameters:

gGraph (optional, default: None): Previous marginal graph.

Returns:

gGraph: New marginal graph, 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

\[\pi_{ij} = \sum_{\boldsymbol A}A_{ij}P(\boldsymbol A|\boldsymbol D)\]

where \(P(\boldsymbol A|\boldsymbol D)\) is the posterior probability given the data.

collect_marginal_multigraph(g=None)#

Collect marginal latent multigraph during MCMC runs.

Parameters:

gGraph (optional, default: None): Previous marginal multigraph.

Returns:

gGraph: New marginal graph, 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

\[\pi_{ij}(w) = \sum_{\boldsymbol G}\delta_{w,G_{ij}}P(\boldsymbol G|\boldsymbol D)\]

where \(P(\boldsymbol G|\boldsymbol D)\) is the posterior probability of a multigraph \(\boldsymbol G\) given the data.

copy(**kwargs)[source]#: Return a copy of the state.

entropy(latent_edges=True, density=True, **kwargs)#: Return the entropy, i.e. negative log-likelihood.

get_block_state()#: Return the underlying block state, which can be either BlockState or NestedBlockState.

get_edge_prob(u, v, entropy_args={}, epsilon=1e-08)#: Return conditional posterior log-probability of edge \((u,v)\).

get_edges_prob(elist, entropy_args={}, epsilon=1e-08)#: Return conditional posterior log-probability of an edge list, with shape \((E,2)\).

get_graph()#: Return the current inferred graph.

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.

set_state(g, w)#

virtual_add_edge(u, v, entropy_args={})#

virtual_remove_edge(u, v, entropy_args={})#

graph_tool.inference.UncertainBlockState

Contents

graph_tool.inference.UncertainBlockState#