{
    "info": {
        "author": "Sadamori Kojaku",
        "author_email": "sadamori.koujaku@bristol.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development"
        ],
        "description": "Python codes for the (q, s)-test, a significance test for individual communities in networks. \n\nPlease cite\n\n  Kojaku, S. and Masuda, N. \"A generalised significance test for individual communities in networks\". Preprint arXiv: 1712.00298 (2017)\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\nContents\n  \n  LICENSE - License of qstest\n  \n  README.md - README file for Github\t\n\n  README.txt - This README file\n\n  setup.py - Script for installing qstest\n \n  requirements.txt - List of libraries installed by setup.py \n\n  test.py - Test code for Travis CI\n\n  .gitignore - Configuration file for GitHub\n  \n  .travis.yml - Configuration file for Travis CI\n  \n  qstest/ - Python codes for the (q, s)-test:\n      \n    qstest/__init__.py - Header file\n  \n    qstest/cdalgorithm_wrapper.py - Codes for community-detection algorithms\n\n    qstest/qstest.py contains - Codes for the (q, s)-test \n\n    qstest/quality_functions.py - Codes for calculating quality functions of a community \n  \n    qstest/size_functions.py - Codes for calculating the size of a community\n  \n  examples/ - example codes:\n    \n    examples/example1.py - Usage of qstest with a built-in quality function, community-size function and community detection algorithm\n\n    examples/example2.py - Usage of qstest with a user-defined quality function\n\n    examples/example3.py - Usage of qstest with a user-defined community-size function \n\n    examples/example4.py - Usage of qstest with a user-defined community-detection algorithm\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\nInstallation\n\n  You can install qstest with pip, a package management system for Python.\n  \n  To install, run \n\n    pip install qstest\n\n  If this does not work, try \n\t\n    python setup.py install\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\nUsage\n \n  sg, p_values = qstest(network, communities, qfunc, sfunc, cdalgorithm, num_of_rand_net = 500, alpha = 0.05, num_of_thread = 4)\n \n  Input \n\n    network - Networkx Graph class instance\n  \n    communities - C-dimensional list of lists. communities[c] is a list containing the IDs of nodes belonging to community c. Node and community indices start from 0.\n  \n    qfunc - Quality of a community. The following quality functions are available:\n  \n      qmod - Contribution of a community to the modularity \n  \n      qint - Internal average degree \n  \n      qexp - Expansion\u3000\n  \u3000\n      qcnd - Conductance\u3000\n  \n      To pass your quality function to qstest, see \"How to pass your quality function to qstest\" below.\n  \n    sfunc - Community-size function (i.e., size of a community). The following community-size functions are available:\n  \n      n - Number of nodes in a community\n   \n      vol - Sum of the degrees of nodes in a community\n      \n      To pass your community-size function to qstest, see \"How to pass your community-size function to qstest\" below.\n     \n    cdalgorithm - Community-detection algorithm. The following algorithms are available:\n  \n      louvain - Louvain algorithm (http://perso.crans.org/aynaud/communities/index.html)\n  \n      label_propagation - Label propagation algorithm (https://networkx.github.io/documentation/stable/reference/algorithms/community.html)\n  \n      To pass your community-detection algorithm to qstest, see \"How to pass your community-detection algorithm to qstest\" below.\n   \n    num_of_rand_net (optional) - Number of randomised networks (Default: 500)\n  \n    alpha (optional) - Statistical significance level before the \u0160id\u00e1k correction (Default: 0.05)\n  \n    num_of_thread (optional) - Maximum number of CPU threads (Default: 4)\n  \n  Output\n\n    sg - Results of the significance test (C-dimensional list). sg[c] = True or False indicates that community c is significant or insignificant, respectively. \n  \n    p_values - P-values for the communities (C-dimensional list). p_values[c] is the p-value for community c. \n  \n  Example (examples/example1.py)\n  \n    import networkx as nx\n    import qstest as qs\n    \n    network = nx.karate_club_graph()\n    communities = qs.louvain(network)\n    sg, p_values = qs.qstest(network, communities, qs.qmod, qs.vol, qs.louvain)\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\nHow to pass your quality function to qstest\n\n  Write a quality function of a community as follows:\n\n    q = my_qfunc(network, community)\n\n    Input\n  \n      network - Networkx Graph class instance\n   \n      community - List of nodes belonging to a community\n  \n    Output\n  \n      q - Quality of the community\n\n  Then, pass my_qfunc to qstest:\n\n    sg, p_values = qstest(network, communities, my_qfunc, sfunc, cdalgorithm)\n\n  Example (examples/example2.py)\n\n    import networkx as nx\n    import qstest as qs\n    \n    # Number of intra-community edges\n    def my_qfunc(network, nodes):\n            return network.subgraph(nodes).size()\n    \n    network = nx.karate_club_graph()\n    communities = qs.louvain(network)\n    sg, p_values = qs.qstest(network, communities, my_qfunc, qs.vol, qs.louvain)\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\nHow to pass your community-size function to qstest\n    \n  Write a community-size function of a community as follows:\n  \n    s = my_sfunc(network, community)\n\n    Input\n\n      network - Networkx Graph class instance\n \n      community - List of the IDs of nodes belonging to a community\n\n    Output\n\n      s - Size of the community\n\n  Then, pass my_sfunc to qstest:\n\n    sg, p_values = qstest(network, communities, qfunc, my_sfunc, cdalgorithm)\n\n  Example (examples/example3.py)\n\n    import networkx as nx\n    import qstest as qs\n    \n    # Square of the number of nodes in a community\n    def my_sfunc(network, nodes):\n            return len(nodes) * len(nodes)\n    \n    network = nx.karate_club_graph()\n    communities = qs.louvain(network)\n    sg, p_values = qs.qstest(network, communities, qs.qmod, my_sfunc, qs.louvain)\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\nHow to pass your community-detection algorithm to qstest\n\n  To pass your community-detection algorithm to qstest, write a wrapper function of the following form:\n   \n    communities = my_cdalgorithm(network)\n\n    Input \n\n      network - Networkx Graph class instance\n    \n    Output\n\n      communities - C-dimensional list of lists. communities[c] is a list containing the IDs of nodes belonging to community c.\n    \n  Then, pass my_cdalgorithm to qstest:\n\n    sg, p_values = qstest(network, communities, qfunc, sfunc, my_cdalgorithm)\n    \n  If the community-detection algorithm requires parameters such as the number of communities, then pass the parameters as global variables, e.g., define a global variable X, then use X within the cdalgorithm.\n  \n  Example (examples/example4.py)\n\n    import networkx as nx\n    import qstest as qs\n    from networkx.algorithms import community as nxcdalgorithm\n    \n    # Wrapper function for async_fluidc implemented in Networkx 2.0\n    def my_cdalgorithm(network):\n            communities = []\n            subnets = nx.connected_component_subgraphs(network)\n            for subnet in subnets:\n                    coms_iter = nxcdalgorithm.asyn_fluidc(subnet, min([C, subnet.order()]), maxiter)\n                    for nodes in iter(coms_iter):\n                           communities.append(list(nodes))\n            return communities\n    \n    # Pareameters of async_fluidc\n    C = 3\n    maxiter = 10\n    \n    network = nx.karate_club_graph()\n    communities = my_cdalgorithm(network)\n    sg, p_values = qs.qstest(network, communities, qs.qmod, qs.vol, my_cdalgorithm)\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\nRequirements\n\n  Python 2.7, 3.4 or later\n\n  SciPy 1.0 or later\n\n  Networkx 2.0 or later\n\n  python-louvain 0.9\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\nLast updated: 29 November 2017",
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