Add initial attempt at pagerank with hacky tests#1
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See networkx scipy.sparse implementation here: https://github.com/networkx/networkx/blob/c39511522b272bf65b6a415169cd48d1bf3ff5c1/networkx/algorithms/link_analysis/pagerank_alg.py#L366-L508 |
Also, optimize if adjacency matrix is iso-valued. I would bring this implementation to a benchmarking shootout!
Also, sparsify vectors. May be a decent idea? Not sure. It probably doesn't matter most of the time, but I guess there's a chance it can make the matrix-vector multiply faster for some inputs. We don't drop 0s from the input matrix, because that would be expensive.
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Here's a subtle question: how should edge weights of 0 be interpreted? If a node only has outgoing edges with weight 0, should it be considered a dangling node? I think the answer has to be yes. I think this is also how networkx implicitly treats it. In one interpretation, edge weights in PageRank are considered edge multiplicity of multigraphs. |
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