It's free while in Beta. Pricing TBD once it graduates from Beta.

I presented the new dispatching feature at PyData Global back in December. The first 30 minutes of the follow video: https://youtu.be/pd-Sbm8lHTc?t=46

It shows how to use the feature, some benchmarks, and how other graph libraries can become plugins to the NetworkX dispatching mechanism.

Great talk!

This slide shows some speedups when using the GraphBLAS [1] backend [2]:

https://i.postimg.cc/mr3mkKtx/100x-Faster-Network-X.png

[1] https://github.com/python-graphblas/python-graphblas

[2] https://github.com/python-graphblas/graphblas-algorithms

If you want to play around with SuiteSparse:GraphBLAS using Python, try python-graphblas (https://python-graphblas.readthedocs.io/). And this is a shameless plug. I'm one of the authors of the Python wrapper.

Whoa! That takes me back. During undergrad I did some work on the KDT Python wrapper for CombinatorialBLAS.

Anyone who is serious about graph analysis in Python using linear algebra should check out GraphBLAS (graphblas.org). The inclusion of semirings makes things like SSSP and BFS extremely elegant on sparse adjacency matrices.

The main implementation is SuiteSparse::GraphBLAS, a C library which has two Python bindings (search for grblas or pygraphblas). Disclosure: I'm the author of grblas. Both are available on conda-forge for easy installation.

If you want to try it out without installing, here is a binder link: https://mybinder.org/v2/gh/metagraph-dev/grblas/HEAD?filepat...

Thanks!

What's the underlying data structure of the graph?