GraphPCA
========
Produces a low-dimensional representation of the input graph.
Calculates the ECTD [1]_ of the graph and reduces its dimension using PCA. The
result is an embedding of the graph nodes as vectors in a low-dimensional
space.
Graph data in this repository is courtesy of
`University of Florida Sparse Matrix Collection <https://www.cise.ufl.edu/research/sparse/matrices/>`_.
Python 3.x and 2.6+.
See the API docs: https://brandones.github.io/graphpca/
Usage
-----
Draw a graph, including edges, from a mat file
::
>>> import scipy.io
>>> import networkx as nx
>>> import graphpca
>>> mat = scipy.io.loadmat('test/bcspwr01.mat')
>>> A = mat['Problem'][0][0][1].todense() # that's just how the file came
>>> G = nx.from_numpy_array(A)
>>> graphpca.draw_graph(G)
.. image:: output/bcspwr01-drawing.png
Get a 2D PCA of a high-dimensional graph and plot it.
::
>>> import networkx as nx
>>> import graphpca
>>> g = nx.erdos_renyi_graph(1000, 0.2)
>>> g_2 = graphpca.reduce_graph(g, 2)
>>> graphca.plot_2d(g_2)
.. image:: output/erg-1000.png
Contributing
------------
Issues and Pull requests are very welcome! [On GitHub](https://github.com/brandones/graphpca).
.. [1] https://www.info.ucl.ac.be/~pdupont/pdupont/pdf/ecml04.pdf
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"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\n`University of Florida Sparse Matrix Collection <https://www.cise.ufl.edu/research/sparse/matrices/>`_.\n\nPython 3.x and 2.6+.\n\nSee the API docs: https://brandones.github.io/graphpca/\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_array(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\nIssues and Pull requests are very welcome! [On GitHub](https://github.com/brandones/graphpca).\n\n.. [1] https://www.info.ucl.ac.be/~pdupont/pdupont/pdf/ecml04.pdf\n\n",
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