cugraph

entists 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 https://github.com/rapidsai/cugraph/search?q=sparse Pandas and scipy and IIRC NumPy have sparse methods; sparse.SparseArray, .sparse.; https://pandas.pydata.org/docs/user_guide/sparse.html#sparse... From https://pandas.pydata.org/docs/user_guide/sparse.html#intera... : > Series.sparse.to_coo() is implemented for transforming a Series with sparse values indexed by a MultiIndex to a scipy.sparse.coo_matrix. NetworkX graph algorithms reference docs https://networkx.org/documentation/stable/reference/algorith... NetworkX Compatibility > Differences in Algorithms https://docs.rapids.ai/api/cugraph/stable/basics/nx_transiti... List of algorithms > Combinatorial algorithms > Graph algorithms: https://en.wikipedia.org/wiki/List_of_algorithms#Graph_algor...