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FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
[article]
2020
arXiv
pre-print
Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is associated with a scalar, GNNs attach a feature tensor to each vertex/edge. This additional feature dimension, along with consequently more complex vertex- and edge-wise computations, has enormous implications on locality and parallelism, which existing graph processing systems fail to exploit. This paper proposes
arXiv:2008.11359v2
fatcat:pm5cdwjlj5bfdk547w3r2lwv7q