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 M-NMF is a graph embedding algorithm which learns a clustering based on features extracted with modularity regularized non-negative matrix factorization. The procedure places nodes in an abstract feature space where the inner product of node features reconstructs a proximity based matrix representation of the graph. M-NMF has a quadratic runtime complexity in the number of nodes. This specific implementation supports GPU use (Tensorflow).
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