Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks

• URL: http://arxiv.org/abs/2006.06909v2
• Date: Tue, 18 Aug 2020 03:12:46 GMT
• Title: Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
• Authors: Katsuhiko Ishiguro and Kenta Oono and Kohei Hayashi
• Abstract summary: A graph neural network (GNN) is a good choice for predicting the chemical properties of molecules. In this paper, we expand an atom representation using Weisfeiler-Lehman embedding. We show WL embedding can replace the first two layers of ReLU GNN with a smaller weight norm.
• Score: 15.662820454886203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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