{
    "info": {
        "author": "Bertrand Charpentier",
        "author_email": "bercha@kth.se",
        "bugtrack_url": null,
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        "description": "paris: Hierarchical graph clustering algorithm and dendrogram processing\n=========================\n\nparis is a Python module that provides an implementation of the hierarchical clustering algorithm for graphs, paris.\nIt provides four algorithms able to process dendrograms in order to extract best clusters, clusterings or distances.\n\nInstallation\n------------\n\nInstall the latest version of cylouvain using ``pip`` ::\n\n    $ pip install pyparis\n\nDependencies\n------------\n\ncylouvain requires:\n\n- Python (>= 2.7 or >= 3.4)\n- NumPy\n- NetworkX\n\nSimple example\n--------------\n\nBuild a simple graph with NetworkX::\n\n    >>> import networkx as nx\n    >>> graph = nx.Graph()\n    >>> graph.add_nodes_from(['a', 'b', 'c', 'd', 'e', 'f'])\n    >>> graph.add_edges_from([('a', 'b'), ('a', 'c'), ('b', 'c'),\n                              ('c', 'd'),\n                              ('d', 'e'), ('d', 'f'), ('f', 'e')])\n\nCompute a partition of the nodes using cylouvain::\n\n    >>> from paris import paris\n    >>> dendrogram = paris(graph)\n\nCompute the best clusters, clusterings and distances::\n\n    >>> best_cluster = best_cluster_cut(dendrogram)\n    >>> best_homogneous_clustering = best_homogeneous_cut(dendrogram)\n    >>> best_heterogneous_clustering = best_heterogeneous_cut(dendrogram)\n    >>> best_distance = best_distance\n\nLicense\n-------\n\nReleased under the MIT license::\n\n   Copyright (C) 2018 Bertrand Charpentier <bercha@kth.se>\n",
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        "keywords": "modularity multi-scale hierarchical graph clustering",
        "license": "Apache License 2.0",
        "maintainer": "",
        "maintainer_email": "",
        "name": "pyparis",
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