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Resolve conflicts in eigenector.py and katz.py due to switch to sparse matrix in adj_matrix. Conflicts: networkx/algorithms/centrality/eigenvector.py networkx/algorithms/centrality/katz.py
Mostly done conversion of directed matrix laplacian to sparse matrix format. The "pagerank" walk is still using dense matrices.
Diags is a new scipy function? Not found in the version on travis for testing.
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OK - I finally got all of the test skipping and rough edges burnished off this one. This converts the matrix-based algorithms to use scipy sparse matrices when possible. There are still places for improvement in style, speed, and efficiency - incidence_matrix can probably be made faster, eigenvector_centrality can use scipy.sparse.eigs (#969) etc. Supporting the use case of not having NumPy and SciPy puts a lot of cruft into the testing code. If we want to keep those as optional packages we might consider moving the algorithms into a common place to make that easier. |
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Switch linalg package to use sparse matrices.
Improve efficiency in to_scipy_sparse_matrix
Addresses #969 and #913