MultiflipMCMCState

class graph_tool.inference.MultiflipMCMCState(entropy_args={})

Bases: EntropyState

Base state that implements multiflip (merge-split) MCMC sweeps.

Methods

get_entropy_args()	Return the current default values for the parameters of the function entropy(), together with other operations that depend on them.
multiflip_mcmc_sweep([beta, c, psingle, ...])	Perform niter sweeps of a Metropolis-Hastings acceptance-rejection MCMC with multiple simultaneous moves (i.e. merges and splits) to sample network partitions.
reset_entropy_args()	Reset the current default values for the parameters of the function entropy(), together with other operations that depend on them.
update_entropy_args(**kwargs)	Update the default values for the parameters of the function entropy() from the keyword arguments, in a stateful way, together with other operations that depend on them.

get_entropy_args()

Return the current default values for the parameters of the function entropy(), together with other operations that depend on them.

multiflip_mcmc_sweep(beta=1.0, c=0.5, psingle=None, psplit=1, pmerge=1, pmergesplit=1, pmovelabel=0, d=0.01, gibbs_sweeps=10, niter=1, entropy_args={}, accept_stats=None, verbose=False, **kwargs)

Perform niter sweeps of a Metropolis-Hastings acceptance-rejection MCMC with multiple simultaneous moves (i.e. merges and splits) to sample network partitions.

Parameters:

betafloat (optional, default: 1.)

Inverse temperature.

cfloat (optional, default: .5)

Sampling parameter c for move proposals: For \(c\to 0\) the blocks are sampled according to the local neighborhood of a given node and their block connections; for \(c\to\infty\) the blocks are sampled randomly. Note that only for \(c > 0\) the MCMC is guaranteed to be ergodic.

psinglefloat (optional, default: None)

Relative probability of proposing a single node move. If None, it will be selected as the number of nodes in the graph.

psplitfloat (optional, default: 1)

Relative probability of proposing a group split.

pmergesplitfloat (optional, default: 1)

Relative probability of proposing a marge-split move.

pmovelabelfloat (optional, default: 0)

Relative probability of proposing a group label move.

dfloat (optional, default: 1)

Probability of selecting a new (i.e. empty) group for a given single-node move.

gibbs_sweepsint (optional, default: 10)

Number of sweeps of Gibbs sampling to be performed (i.e. each node is attempted once per sweep) to refine a split proposal.

niterint (optional, default: 1)

Number of sweeps to perform. During each sweep, a move attempt is made for each node, on average.

entropy_argsdict (optional, default: {})

Entropy arguments, with the same meaning and defaults as in graph_tool.inference.BlockState.entropy().

accept_statsdict (optional, default: None)

If provided, this dictionary will be updated with the proposal and acceptance counts for each kind of move.

verbosebool (optional, default: False)

If verbose == True, detailed information will be displayed.

Returns:

dSfloat

Entropy difference after the sweeps.

nattemptsint

Number of vertex moves attempted.

nmovesint

Number of vertices moved.

Notes

This algorithm has an \(O(E)\) complexity, where \(E\) is the number of edges (independent of the number of groups).

References

[peixoto-merge-split-2020]

Tiago P. Peixoto, “Merge-split Markov chain Monte Carlo for community detection”, Phys. Rev. E 102, 012305 (2020), DOI: 10.1103/PhysRevE.102.012305 [sci-hub, @tor], arXiv: 2003.07070

reset_entropy_args()

Reset the current default values for the parameters of the function entropy(), together with other operations that depend on them.

update_entropy_args(**kwargs)

Update the default values for the parameters of the function entropy() from the keyword arguments, in a stateful way, together with other operations that depend on them.

Values updated in this manner are preserved by the copying or pickling of the state.