graphblas-algorithms VS cugraph

And algorithms for the NetworkX backend graphblas-algorithms is here: https://github.com/python-graphblas/graphblas-algorithms

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 :)

GraphBLAS is underrated and underused IMHO. If you use e.g. scipy.sparse, NetworkX, or similar, you should check out GraphBLAS. It is really fast even compared to scipy.sparse, and more capable in many ways.

They've actually started implementing the NetworkX API

https://github.com/python-graphblas/graphblas-algorithms

with python-graphblas

https://github.com/python-graphblas/python-graphblas

cuGraph does some of what Networkx does, but it is far from being as easy to use. But it should be fast.

https://en.wikipedia.org/wiki/Sparse_matrix :

> The concept of sparsity is useful in combinatorics and application areas such as network theory and numerical analysis, which typically have a low density of significant data or connections. Large sparse matrices often appear in scientific or engineering applications when solving partial differential equations.

CuGraph has a NetworkX-like API, though only so many of the networkx algorithms are CUDA-optimized.

https://github.com/rapidsai/cugraph :

> cuGraph operates, at the Python layer, on GPU DataFrames, thereby allowing for seamless passing of data between ETL tasks in cuDF and machine learning tasks in cuML. Data scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS.

> While the high-level cugraph python API provides an easy-to-use and familiar interface for data scientists that's consistent with other RAPIDS libraries in their workflow, some use cases require access to lower-level graph theory concepts. For these users, we provide an additional Python API called pylibcugraph, intended for applications that require a tighter integration with cuGraph at the Python layer with fewer dependencies. Users familiar with C/C++/CUDA and graph structures can access libcugraph and libcugraph_c for low level integration outside of python.

/? sparse

I feel like you would need to develop a custom solution which might in part store data in Neo4j but you will have to figure out how to efficiently pull the data you need to train your GNNs; and I think this tends to be the bottleneck since Graph DBs are not optimised for the kinds of queries you need for GNNs. For what it's worth, I wouldn't really bother with implementing a custom graph data structure (unless I was really keen) as there are some good implementations out there. Have you looked at cuGraph for example?