Geometric Graph Convolutional Neural Networks

By Przemyslaw Spurek, Tomasz Danel, Jacek Tabor, Marek Śmieja, Lukasz Struski, Agnieszka Słowik, Lukasz Maziarka. 2019

10 citations recorded by the source corpus.

Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds. However, GCNs lack the ability to take into account the ordering of node neighbors, even when there is a geometric interpretation of the graph vertices that provides an order based on their spatial positions. To remedy this issue, we propose Geometric Graph Convolutional Network (geo-GCN) which uses spatial features to efficiently learn from graphs that can be naturally located in space. Our contribution is threefold: we propose a GCN-inspired architecture which (i) leverages node positions, (ii) is a proper generalisation of both GCNs and Convolutional Neural Networks (CNNs), (iii) benefits from augmentation which further improves the performance and assures invariance with respect to the desired properties. Empirically, geo-GCN outperforms state-of-the-art graph-based methods on image classification and chemical tasks.

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Citations in the corpus

Learning from Protein Structure with Geometric Vector Perceptrons (2020, background): “To address this, some recent work on small molecules aims to leverage geometric features in GNNs through voxelized convolutions at each graph node [25], message transformation based on encoded angle information [17] or products with position vectors to account for vector features [7].”