Produces a low-dimensional representation of the input graph.

Calculates the ECTD¹ 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 the mind-blowingly cool University of Florida Sparse Matrix Collection.

Python 3.x and 2.6+.

See the API docs: https://brandones.github.io/graphpca/

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)

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)

Issues and Pull requests are very welcome! [On GitHub](https://github.com/brandones/graphpca).