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Poster
Scalable Graph Neural Networks via Bidirectional Propagation
Ming Chen · Zhewei Wei · Bolin Ding · Yaliang Li · Ye Yuan · Xiaoyong Du · Ji-Rong Wen

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1839
in Poster Session 7 »

Graph Neural Networks (GNN) are an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use "graph sampling" or "layer-wise sampling" techniques to reduce training time; However, these methods still suffer from degrading performance and scalability problems when applying to graphs with billions of edges. In this paper, we present GBP, a scalable GNN that utilizes a localized bidirectional propagation process from both the feature vector and the training/testing nodes. Theoretical analysis shows that GBP is the first method that achieves sub-linear time complexity for both the precomputation and the training phases. An extensive empirical study demonstrates that GBP achieves state-of-the-art performance with significantly less training/testing time. Most notably, GBP is able to deliver superior performance on a graph with over 60 million nodes and 1.8 billion edges in less than 2,000 seconds on a single machine.

[ Paper ]

Author Information

Ming Chen (Renmin University of China)
Zhewei Wei (Renmin University of China)
Bolin Ding ("Data Analytics and Intelligence Lab, Alibaba Group")
Yaliang Li (Alibaba Group)
Ye Yuan ( Beijing Institute of Technology)
Xiaoyong Du (Renmin University of China)
Ji-Rong Wen (Renmin University of China)

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