Scaling Graph Neural Networks with Approximate PageRank

Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

197 Zitate (Scopus)

Abstract

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 trivially parallelized for large datasets like those found in industry settings. We demonstrate that PPRGo outperforms baselines in both distributed and single-machine training environments on a number of commonly used academic graphs. To better analyze the scalability of large-scale graph learning methods, we introduce a novel benchmark graph with 12.4 million nodes, 173 million edges, and 2.8 million node features. We show that training PPRGo from scratch and predicting labels for all nodes in this graph takes under 2 minutes on a single machine, far outpacing other baselines on the same graph. We discuss the practical application of PPRGo to solve large-scale node classification problems at Google.

OriginalspracheEnglisch
TitelKDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Herausgeber (Verlag)Association for Computing Machinery
Seiten2464-2473
Seitenumfang10
ISBN (elektronisch)9781450379984
DOIs
PublikationsstatusVeröffentlicht - 23 Aug. 2020
Veranstaltung26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 - Virtual, Online, USA/Vereinigte Staaten
Dauer: 23 Aug. 202027 Aug. 2020

Publikationsreihe

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Konferenz

Konferenz26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Land/GebietUSA/Vereinigte Staaten
OrtVirtual, Online
Zeitraum23/08/2027/08/20

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