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        "author": "Benjamin Maier",
        "author_email": "bfmaier@physik.hu-berlin.de",
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        "description": "General Topological Overlap Measure\n===================================\n\nThis package takes an undirected unweighted scipy.sparse adjacency\nmatrix as an input and computes the GTOM(m) method, using m+1-step\nneighbors (1). It's highly efficient and can be used for parallel\ncomputation by calling the function for only few nodes at a time.\n\n(1) `*Gene network interconnectedness and the generalized topological\noverlap\nmeasure* <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.93.329&rep=rep1&type=pdf>`__,\nA. M. Yip and S. Horvath, BMC Bioinformatics 2007 8:22\n\nInstall\n-------\n\n.. code:: bash\n\n    $ pip install gtom\n\nExample\n-------\n\nRecreating Panels a and c of Figure 3 of the technical report [1]:\n\n.. code:: python\n\n    #!python\n\n    import matplotlib.pyplot as pl\n    import networkx as nx\n    from gtom import gtom\n    import scipy.sparse as sprs\n    import numpy as np\n\n    edges = [(0,1),(1,2),(0,3),(0,4),(0,5),(0,7),\n             (1,3),(1,4),(1,6),(1,8),(1,9),(1,10),\n             (5,6),(7,8)]\n    G = nx.Graph()\n    G.add_edges_from(edges)\n\n    pos = nx.spring_layout(G)\n    labels = { n:str(n+1) for n in G.nodes()}\n    nx.draw(G,pos=pos)\n    nx.draw_networkx_labels(G,pos=pos,labels=labels)\n\n    N = G.number_of_nodes()\n    edges = np.array(edges,dtype=int)\n    A = sprs.csc_matrix((np.ones((edges.shape[0],)),(edges[:,0],edges[:,1])),dtype=float,shape=(N,N))\n    A += A.T\n\n    print(\"recreate results from figure 3 in [1]\")\n    print(\"       |\\t(i,j)=(1,2)\\t(i,j)=(1,3)\\t(i,j)=(2,3)\")\n    print(\"---------------------------------------------------------\")\n    for m in range(3):\n        T = gtom(A,m)\n        print(\" m = %d |\\t%f\\t%f\\t%f\" %(m,T[0,1],T[0,2],T[1,2]))\n\n    pl.show()",
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