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Scaling Graph Neural Networks with Approximate PageRank. (arXiv:2007.01570v2 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Graph neural networks (GNNs) have emerged as a powerful approach for solving
many network mining tasks. However, learning on large graphs remains a
challenge - many recently proposed scalable GNN approaches rely on an expensive
message-passing procedure to propagate information through the graph. We
present the PPRGo model which utilizes an efficient approximation of
information diffusion in GNNs resulting in significant speed gains while
maintaining state-of-the-art prediction performance. In addition to being
faster, PPRGo is inherently scalable, and can be …
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