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        "description": "cylouvain: Cython Louvain\n=========================\n\ncylouvain is a Python module that provides a fast implementation\nof the classic Louvain algorithm for node clustering in graph.\n\nThis module uses Cython in order to obtain C-like performance with\ncode mostly writen in Python.\n\nInstallation\n------------\n\nInstall the latest version of cylouvain using ``pip`` ::\n\n    $ pip install cylouvain\n\nDependencies\n------------\n\ncylouvain requires:\n\n- Python (>= 2.7 or >= 3.4)\n- NumPy\n- SciPy\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'])\n    >>> graph.add_edges_from([('a', 'b'), ('a', 'c'), ('b', 'c'),\n                              ('c', 'd'), ('c', 'e'), ('d', 'e')])\n\nCompute a partition of the nodes using cylouvain::\n\n    >>> import cylouvain\n    >>> partition = cylouvain.best_partition(graph)\n    >>> print(partition)\n    {'a': 0, 'b': 0, 'c': 0, 'd': 1, 'e': 1}\n\nCompute the corresponding modularity::\n\n    >>> modularity = cylouvain.modularity(partition, graph)\n    >>> print(\"Modularity: %0.3f\\n\" % modularity)\n    Modularity: 0.111\n\nReferences\n----------\n\nThe Louvain algorithm is an heuristic to find a node partition that maximizes the modularity function.\nIt is described in::\n\n    Fast unfolding of communities in large networks\n    Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre\n    Journal of Statistical Mechanics: Theory and Experiment 2008 (10), P10008 (12pp)\n\nThe modularity function was first introduced in::\n\n    Finding and evaluating community structure in networks\n    Newman, Mark EJ and Girvan, Michelle\n    Physical review E, 2004, vol. 69, no 2, p. 026113.\n\nLicense\n-------\n\nReleased under the 3-Clause BSD license (see `COPYING`)::\n\n   Copyright (C) 2018 Alexandre Hollocou <alexandre@hollocou.fr>",
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