pygraphblas author here. scipy.sparse and graphblas are superficially similar in that they provide a sparse matrix type, but scipy.sparse is oriented more toward mathematical matrix computation and provides a wonderful and huge bag of tools to work with, for example, solving and doing LU decomposition, see scipy.sparse.linalg.

graphblas provides a different bag of tools oriented toward solving graph problems, primarily around semirings and custom graph element operation. I don't think scipy.sparse, for example, provides a means for plugging in different semiring operations for computing shortest paths or max flow. On the other hand, graphblas does not provide decomposition or solving algorithms like scipy.sparse does.

It is my goal to make it so that scipy.sparse matrices, dense numpy matrices, and networkX graphs can be interchangeable used with pygraphblas in the most reasonably efficient way. This way we get all the tools in both bags. Would love to chat with more scipy developers to see where there can be unification.

UPDATE: it looks like there is some support for graph solving with scipy.sparse.csgraph, would be interesting to see how these compare to GraphBLAS based approaches!

Tim Davis also wrote the underlying code for much of scipy.sparse (his code is underneath almost all sparse matrix libs [1]). See...

[1] Tim Davis Research http://faculty.cse.tamu.edu/davis/research.html

Oh, what a man! He's also the author of the legendary CHOLMOD algorithm, probably one of the most used numerical algorithms ever, just after the FFT.