Web: http://arxiv.org/abs/2205.04711

May 11, 2022, 1:11 a.m. | Yunjae Lee, Jinha Chung, Minsoo Rhu

cs.LG updates on arXiv.org arxiv.org

Graph neural networks (GNNs) can extract features by learning both the
representation of each objects (i.e., graph nodes) and the relationship across
different objects (i.e., the edges that connect nodes), achieving
state-of-the-art performance in various graph-based tasks. Despite its
strengths, utilizing these algorithms in a production environment faces several
challenges as the number of graph nodes and edges amount to several billions to
hundreds of billions scale, requiring substantial storage space for training.
Unfortunately, state-of-the-art ML frameworks employ an in-memory …

ar arxiv graph graph neural networks networks neural neural networks processing scale storage training

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