Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks

Katsuhiko Ishiguro; Kenta Oono; Kohei Hayashi

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arXiv · 2006.06909

Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks

Katsuhiko Ishiguro·Kenta Oono·Kohei Hayashi

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Abstract

A graph neural network (GNN) is a good choice for predicting the chemical properties of molecules. Compared with other deep networks, however, the current performance of a GNN is limited owing to the "curse of depth." Inspired by long-established feature engineering in the field of chemistry, we expanded an atom representation using Weisfeiler-Lehman (WL) embedding, which is designed to capture local atomic patterns dominating the chemical properties of a molecule. In terms of representability, we show WL embedding can replace the first two layers of ReLU GNN -- a normal embedding and a hidden GNN layer -- with a smaller weight norm. We then demonstrate that WL embedding consistently improves the empirical performance over multiple GNN architectures and several molecular graph datasets.

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Katsuhiko Ishiguro, Kenta Oono, Kohei Hayashi. 2020-06-12. Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks. https://arxiv.org/abs/2006.06909

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