I have a large (>50,000 node) weighted, directed network, which is sparse. It is currently in Scipy's sparse matrix format:
type(data) scipy.sparse.csc.csc_matrix So it's not big at all. However, my typical way of getting these matrices into igraph has been: g = Graph.Weighted_Adjacency( data.toarray().tolist() ) data.toarray() converts the sparse matrix to dense/full format, which blows my memory out of the water. What ought I to be doing? Thanks!
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