#! /usr/bin/env python # -*- coding: utf-8 -*- # # graph_tool -- a general graph manipulation python module # # Copyright (C) 2006-2025 Tiago de Paula Peixoto # # This program is free software; you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the Free # Software Foundation; either version 3 of the License, or (at your option) any # later version. # # This program is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more # details. # # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . """ ``graph_tool.inference`` ------------------------ This module contains algorithms for the identification of large-scale network structure via the statistical inference of generative models. .. note:: An introduction to the concepts used here, as well as a basic HOWTO is included in the cookbook section: :ref:`inference-howto`. Nonparametric stochastic block model inference ++++++++++++++++++++++++++++++++++++++++++++++ High-level functions ==================== .. autosummary:: :nosignatures: :toctree: autosummary minimize_blockmodel_dl minimize_nested_blockmodel_dl State classes ============= .. currentmodule:: graph_tool.inference .. autosummary:: :nosignatures: :toctree: autosummary :template: class.rst BlockState OverlapBlockState LayeredBlockState NestedBlockState PPBlockState RankedBlockState ModularityState NormCutState TemperingState CliqueState Abstract base classes ===================== .. currentmodule:: graph_tool.inference .. autosummary:: :nosignatures: :toctree: autosummary :template: class.rst EntropyState MCMCState MultiflipMCMCState MultilevelMCMCState GibbsMCMCState MulticanonicalMCMCState ExhaustiveSweepState DrawBlockState Sampling and minimization ========================= .. autosummary:: :nosignatures: :toctree: autosummary mcmc_equilibrate mcmc_anneal multicanonical_equilibrate .. autosummary:: :nosignatures: :toctree: autosummary :template: class.rst MulticanonicalState Comparing and manipulating partitions ===================================== .. autosummary:: :nosignatures: :toctree: autosummary :template: class.rst PartitionModeState ModeClusterState PartitionCentroidState .. autosummary:: :nosignatures: :toctree: autosummary partition_overlap nested_partition_overlap variation_information mutual_information reduced_mutual_information contingency_graph shuffle_partition_labels order_partition_labels order_nested_partition_labels align_partition_labels align_nested_partition_labels partition_overlap_center nested_partition_overlap_center nested_partition_clear_null contiguous_map nested_contiguous_map Auxiliary functions =================== .. autosummary:: :nosignatures: :toctree: autosummary mf_entropy bethe_entropy microstate_entropy marginal_multigraph_entropy half_edge_graph get_block_edge_gradient Auxiliary classes ================= .. autosummary:: :nosignatures: :toctree: autosummary :template: class-no-inherit.rst PartitionHist BlockPairHist Nonparametric network reconstruction ++++++++++++++++++++++++++++++++++++ Reconstruction from direct measurements ======================================= .. autosummary:: :nosignatures: :toctree: autosummary :template: class.rst LatentLayerBaseState LatentMultigraphBlockState LatentClosureBlockState MeasuredBlockState MeasuredClosureBlockState MixedMeasuredBlockState UncertainBlockState UncertainBaseState Expectation-maximization inference ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autosummary:: :nosignatures: :toctree: autosummary latent_multigraph Reconstruction from dynamics and behavior ========================================= .. autosummary:: :nosignatures: :toctree: autosummary :template: class.rst DynamicsBlockStateBase BPBlockStateBase PseudoNormalBlockState NormalGlauberBlockState LinearNormalBlockState LVBlockState EpidemicsBlockState IsingBlockStateBase IsingGlauberBlockState CIsingGlauberBlockState PseudoIsingBlockState PseudoCIsingBlockState Semiparametric stochastic block model inference +++++++++++++++++++++++++++++++++++++++++++++++ State classes ============= .. autosummary:: :nosignatures: :toctree: autosummary :template: class.rst EMBlockState Expectation-maximization Inference ================================== .. autosummary:: :nosignatures: :toctree: autosummary em_infer Large-scale descriptors +++++++++++++++++++++++ .. autosummary:: :nosignatures: :toctree: autosummary modularity """ __all__ = ["minimize_blockmodel_dl", "minimize_nested_blockmodel_dl", "BlockState", "OverlapBlockState", "LayeredBlockState", "NestedBlockState", "PPBlockState", "RankedBlockState", "PartitionCentroidState", "PartitionModeState", "ModeClusterState", "ModularityState", "NormCutState", "LatentMultigraphBlockState", "UncertainBlockState", "MeasuredBlockState", "UncertainBaseState", "MixedMeasuredBlockState", "DynamicsBlockStateBase", "EpidemicsBlockState", "BPBlockStateBase", "IsingBlockStateBase", "IsingGlauberBlockState", "PseudoIsingBlockState", "CIsingGlauberBlockState", "PseudoCIsingBlockState", "PseudoNormalBlockState", "LinearNormalBlockState", "NormalGlauberBlockState", "LVBlockState", "TestBlockState", "LatentLayerBaseState", "LatentClosureBlockState", "MeasuredClosureBlockState", "HistState", "CliqueState", "EntropyState", "MCMCState", "MultiflipMCMCState", "MultilevelMCMCState", "GibbsMCMCState", "MulticanonicalMCMCState", "ExhaustiveSweepState", "DrawBlockState", "mcmc_equilibrate", "mcmc_anneal", "TemperingState", "multicanonical_equilibrate", "MulticanonicalState", "EMBlockState", "em_infer", "mf_entropy", "bethe_entropy", "microstate_entropy", "marginal_multigraph_entropy", "marginal_multigraph_sample", "marginal_graph_sample", "marginal_multigraph_lprob", "marginal_graph_lprob", "PartitionHist", "BlockPairHist", "half_edge_graph", "get_block_edge_gradient", "get_hierarchy_tree", "modularity", "latent_multigraph", "partition_overlap", "nested_partition_overlap", "contingency_graph", "contiguous_map", "nested_contiguous_map", "align_partition_labels", "align_nested_partition_labels", "shuffle_nested_partition_labels", "shuffle_partition_labels", "order_partition_labels", "order_nested_partition_labels", "partition_overlap_center", "nested_partition_overlap_center", "nested_partition_clear_null", "variation_information", "mutual_information", "reduced_mutual_information"] from . base_states import * from . blockmodel import * from . overlap_blockmodel import * from . layered_blockmodel import * from . nested_blockmodel import * from . uncertain_blockmodel import * from . reconstruction import * from . mcmc import * from . minimize import * from . blockmodel_em import * from . util import * from . modularity import * from . norm_cut import * from . latent_multigraph import * from . partition_centroid import * from . partition_modes import * from . planted_partition import * from . ranked import * from . latent_layers import * from . histogram import * from . clique_decomposition import * libinference.init_cache() from .. openmp import _openmp_add_hook _openmp_add_hook(libinference.init_cache)