dallathee

GraphBLAS is wrapped by https://github.com/python-graphblas/python-graphblas/ and the algorithms are available at https://github.com/python-graphblas/graphblas-algorithms. NetworkX only dispatches the computation for a subset of algorithms to graphblas-algorithms right now.

If you want the graphblas API in python, https://github.com/python-graphblas/python-graphblas/ is the right place :)

> Is it now finally faster than GraphLab?

NetworkX(pure python with dict as the data structure) itself will never be faster than any C++ based library (I think GraphLab doesn't even exist now?) but with the new experimental backend plugins, you should be able to use NetworkX API and dispatch the computation to more efficient backends like GraphBLAS, and hopefully CuGraph in the future (if you have GPUs). A bit more info: https://twitter.com/networkx_team/status/1612478129649459202

A good place to start specifically for NetworkX would be to go through the new contributor documentation: https://networkx.org/documentation/latest/developer/new_cont...

We also have some structured projects https://networkx.org/documentation/latest/developer/projects... but they are usually for programs like GSoC/Outreachy.

Feel free to start a discussion https://github.com/networkx/networkx/discussions if you are looking for something specific :)

[I am one of the NetworkX devs]

Checkout https://github.com/jupyterlite/jupyterlite, WASM'd Jupyter in the browser with Pyodide

There is igraph, graph-tool, snap and even a CUDA enabled one (cuGraph, they try to follow networkx API).

The Journal of Open Source Software https://joss.theoj.org is a nice example of this.

NASA budget is 0.489% of US 2019 Budget, ISRO is ~0.4% of Indian 2019 Budget.

Also ISRO makes a profit through its commercial satellite launching missions. https://en.wikipedia.org/wiki/Antrix_Corporation