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Graph Neural Network (GNN) is a popular architecture for the analysis ofchemical molecules, and it has numerous applications in material and medicinalscience. Current lines of GNNs developed for molecular analysis, however, donot fit well on the training set, and their performance does not scale wellwith the complexity of the network. In this paper, we propose an auxiliarymodule to be attached to a GNN that can boost the representation power of themodel without hindering with the original GNN architecture. Our auxiliarymodule can be attached to a wide variety of GNNs, including those that are usedcommonly in biochemical applications. With our auxiliary architecture, theperformances of many GNNs used in practice improve more consistently, achievingthe state-of-the-art performance on popular molecular graph datasets.

Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis