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arXiv · 2203.11683

Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification

Zhaohui Wang·Qi Cao·Huawei Shen·Bingbing Xu·Xueqi Cheng
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Abstract

The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test. To achieve high expressive GNNs beyond WL test, we propose a novel graph isomorphism test method, namely Twin-WL, which simultaneously passes node labels and node identities rather than only passes node label as WL. The identity-passing mechanism encodes complete structure information of rooted subgraph, and thus Twin-WL can offer extra power beyond WL at distinguishing graph structures. Based on Twin-WL, we implement two Twin-GNNs for graph classification via defining readout function over rooted subgraph: one simply readouts the size of rooted subgraph and the other readouts rich structure information of subgraph following a GNN-style. We prove that the two Twin-GNNs both have higher expressive power than traditional message passing GNNs. Experiments also demonstrate the Twin-GNNs significantly outperform state-of-the-art methods at the task of graph classification.

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Zhaohui Wang, Qi Cao, Huawei Shen, Bingbing Xu, Xueqi Cheng. 2022-03-22. Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification. https://arxiv.org/abs/2203.11683

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