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

Python 3.x and 2.6+.

## 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_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)

## 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 |