Produces a low-dimensional representation of the input graph.
Calculates the ECTD  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.
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'].todense() # that's just how the file came >>> G = nx.from_numpy_matrix(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)
Feel free to fork me and create a pull request at https://github.com/brandones/graphpca