Isometric Transformation Invariant and Equivariant Graph Convolutional Networks

• URL: http://arxiv.org/abs/2005.06316v4
• Date: Wed, 10 Mar 2021 12:41:51 GMT
• Title: Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
• Authors: Masanobu Horie, Naoki Morita, Toshiaki Hishinuma, Yu Ihara, Naoto Mitsume
• Abstract summary: We propose a set of transformation invariant and equivariant models based on graph convolutional networks, called IsoGCNs. We demonstrate that the proposed model has a competitive performance compared to state-of-the-art methods on tasks related to geometrical and physical simulation data.
• Score: 5.249805590164902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphs are one of the most important data structures for representing pairwise relations between objects. Specifically, a graph embedded in a Euclidean space is essential to solving real problems, such as physical simulations. A crucial requirement for applying graphs in Euclidean spaces to physical simulations is learning and inferring the isometric transformation invariant and equivariant features in a computationally efficient manner. In this paper, we propose a set of transformation invariant and equivariant models based on graph convolutional networks, called IsoGCNs. We demonstrate that the proposed model has a competitive performance compared to state-of-the-art methods on tasks related to geometrical and physical simulation data. Moreover, the proposed model can scale up to graphs with 1M vertices and conduct an inference faster than a conventional finite element analysis, which the existing equivariant models cannot achieve.

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