{ "info": { "author": "Brandon Istenes", "author_email": "brandonesbox@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "License :: OSI Approved :: Apache Software License" ], "description": "graphpca\n===========\n\nProduces a low-dimensional representation of the input graph.\n\nCalculates the ECTD [1]_ of the graph and reduces its dimension using PCA. The\nresult is an embedding of the graph nodes as vectors in a low-dimensional\nspace.\n\nGraph data in this repository is courtesy of the mind-blowingly cool\n`University of Florida Sparse Matrix Collection `_.\n\nUsage\n-----\n\nDraw a graph, including edges, from a mat file\n::\n\n >>> import scipy.io\n >>> import networkx as nx\n >>> import graphpca\n >>> mat = scipy.io.loadmat('test/bcspwr01.mat')\n >>> A = mat['Problem'][0][0][1].todense() # that's just how the file came\n >>> G = nx.from_numpy_matrix(A)\n >>> graphpca.draw_graph(G)\n\n.. image:: output/bcspwr01-drawing.png\n\nGet a 2D PCA of a high-dimensional graph and plot it.\n::\n\n >>> import networkx as nx\n >>> import graphpca\n >>> g = nx.erdos_renyi_graph(1000, 0.2)\n >>> g_2 = graphpca.reduce_graph(g, 2)\n >>> graphca.plot_2d(g_2)\n\n.. image:: output/erg-1000.png\n\n\nContributing\n------------\n\nFeel free to fork me and create a pull request at\nhttps://github.com/brandones/graphpca\n\n.. 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