Your Neighbors Are Communicating: Towards Powerful and Scalable Graph Neural Networks

Meng Liu, Haiyang Yu, Shuiwang Ji

Keywords: Graph neural networks, expressiveness, graph isomorphism test

TL;DR: We propose a general GNN framework with provably expressive power, while maintaining the scalability of the message passing scheme.

Abstract: Message passing graph neural networks (GNNs) are known to have their expressiveness upper-bounded by 1-dimensional Weisfeiler-Lehman (1-WL) algorithm. To achieve more powerful GNNs, existing attempts either require ad hoc features, or involve operations that incur high time and space complexities. In this work, we propose a general and provably powerful GNN framework that preserves the scalability of message passing scheme. In particular, we first propose to empower 1-WL for graph isomorphism test by considering edges among neighbors, giving rise to NC-1-WL. The expressiveness of NC-1-WL is shown to be strictly above 1-WL and below 3-WL theoretically. Further, we propose the NC-GNN framework as a differentiable neural version of NC-1-WL. Our simple implementation of NC-GNN is provably as powerful as NC-1-WL. Experiments demonstrate that our NC-GNN achieves remarkable performance on various benchmarks.

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