As the amount of data available to everyone in the world increases, the ability for a consumer to make informed decisions becomes increasingly difficult. Fortunately, large datasets are a beneficial component for recommendation systems, which can make a sometimes overwhelming decision much easier. Graphs are excellent choices for modeling the relationships inherent in the data that fuel…
]]>NetworkX accelerated by NVIDIA cuGraph is a newly released backend co-developed with the NetworkX team. NVIDIA cuGraph provides GPU acceleration for popular graph algorithms such as PageRank, Louvain, and betweenness centrality. Depending on the algorithm and graph size, it can significantly accelerate NetworkX workflows, up to 50x, even 500x over NetworkX on CPU. In this post…
]]>NetworkX states in its documentation that it is “…a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.” Since its first public release in 2005, it’s become the most popular Python graph analytics library available. This may explain why NetworkX amassed 27M PyPI downloads just in September of 2023. How is NetworkX able to…
]]>