Thanks!
For future users, it looks like this:
sources, targets = data.nonzero()
weights = data[sources, targets]
g = Graph(zip(sources, targets), directed=True, edge_attrs={'weight':
weights})
On Wed, Jul 3, 2013 at 11:51 AM, Tamás Nepusz <[email protected]> wrote:
> > data.toarray() converts the sparse matrix to dense/full format, which
> blows my memory out of the water. What ought I to be doing?
> Use the nonzero() method of your sparse matrix to extract the row/column
> indices of the nonzero elements of your matrix, then construct the graph
> using the standard Graph() constructor which accepts an edge list. If you
> want to keep the weights as well, there should be a way in SciPy to extract
> the values of the nonzero elements in the same order (probably
> A[A.nonzero()] will do the trick) and then you can assign that vector to
> the "weight" attribute of your graph.
>
> --
> T.
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>
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