TY - GEN
T1 - Scaling Graph Neural Networks with Approximate PageRank
AU - Bojchevski, Aleksandar
AU - Klicpera, Johannes
AU - Perozzi, Bryan
AU - Kapoor, Amol
AU - Blais, Martin
AU - Rózemberczki, Benedek
AU - Lukasik, Michal
AU - Günnemann, Stephan
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/8/23
Y1 - 2020/8/23
N2 - 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.
AB - 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.
KW - graph neural networks
KW - personalized pagerank
KW - scalability
UR - http://www.scopus.com/inward/record.url?scp=85090412772&partnerID=8YFLogxK
U2 - 10.1145/3394486.3403296
DO - 10.1145/3394486.3403296
M3 - Conference contribution
AN - SCOPUS:85090412772
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2464
EP - 2473
BT - KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Y2 - 23 August 2020 through 27 August 2020
ER -