Package: pycombo
pyCOMBOpyCombo is a python wrapper aroundC++ implementationof the [network] community detection algorithm called "Combo".Details of the algorithm are described in the paper "General optimization technique for high-quality community detection":Sobolevsky, S., Campari, R., Belyi, A. and Ratti, C., 2014. General optimization technique for high-quality community detection in complex networks. Physical Review E, 90(1), p.012811.InstallationYou can install the latest release of pycombo directly from PyPI by executing this:python-mpipinstallpycomboQuick StartThe basic usage is as follows:importpycomboimportnetworkxasnxpartition=pycombo.execute(nx.karate_club_graph())Package supportsNetworkXgraphs and.netfiles. Algorythm uses modularity score as a loss function,
but you can use your own metrics as node weights withtreat_as_modularity=TrueparameterParametersgraph:nx.Graphobject, or string treated as path to Pajek.netfile.weight:Optional[str], defaults toweight. Graph edges property to use as weights. IfNone, graph assumed to be unweighted.
Ignored if graph is passed as string (path to the file), or such property does not exist.max_communities:Optional[int], defaults toNone. Maximum number of communities. If <= 0 or None, assume to be infinite.modularity_resolution:float, defaults to 1.0. Modularity resolution parameter.num_split_attempts:int, defaults to 0. Number of split attempts. If 0, autoadjust this number automatically.fixed_split_step_int, defaults to 0. Step number to apply predefined split. If 0, use only random splits. if >0, sets up the usage of 6 fixed type splits on every fixed_split_step.start_separate: bool, default False. Indicates if Combo should start from assigning each node into its own separate community. This could help to achieve higher modularity, but it makes execution much slower.treat_as_modularity: bool, default False. Indicates if edge weights should be treated as modularity scores. If True, the algorithm solves clique partitioning problem over the given graph, treated as modularity graph (matrix). For example, this allows users to provide their own custom 'modularity' matrix.modularity_resolutionis ignored in this case.verbose: int, defaults to 0. Indicates how much progress information Combo should print out. For now Combo has only one level starting at verbose >= 1.intermediate_results_path: Optional str, defaults to None. Path to the file where community assignments will be saved on each iteration. If None or empty, intermediate results will not be saved.return_modularity: bool, defaults toTrue. Indicates if function should return achieved modularity score.random_seed: int, defaults to None. Random seed to use. None indicates using some internal default value that is based on time and is expected to be different for each call.Returnspartition :Dict{int : int}, community labels for each node.modularity :float. Achieved modularity value. Only returned ifreturn_modularity=True.More examples can be found inexamplefolder.DevelopmentThis repo useshttps://github.com/Alexander-Belyi/Comboas agit submodule.
So for local development, clone with--recurse-submodulesflag, as:gitclone--recurse-submoduleshttps://github.com/Casyfill/pyComboOr, if you've already cloned it without--recurse-submodules, run:gitsubmoduleupdate--init--recursivePackage is built and managed viapoetry.to install dev version, runpoetry installTo build distributions runpoetry build.Informationproject web_sitepaper