Optimizing Memory and Retrieval for Graph Neural Networks with WholeGraph, Part 1 – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-03-24T20:52:54Z http://www.open-lab.net/blog/feed/ Dongxu Yang <![CDATA[Optimizing Memory and Retrieval for Graph Neural Networks with WholeGraph, Part 1]]> http://www.open-lab.net/blog/?p=79288 2024-04-09T23:45:29Z 2024-03-08T22:13:55Z Graph neural networks (GNNs) have revolutionized machine learning for graph-structured data. Unlike traditional neural networks, GNNs are good at capturing...]]> Graph neural networks (GNNs) have revolutionized machine learning for graph-structured data. Unlike traditional neural networks, GNNs are good at capturing...An illustration representing WholeGraph.

Graph neural networks (GNNs) have revolutionized machine learning for graph-structured data. Unlike traditional neural networks, GNNs are good at capturing intricate relationships in graphs, powering applications from social networks to chemistry. They shine particularly in scenarios like node classification, where they predict labels for graph nodes, and link prediction, where they determine the��

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