Rewiring Networks for Graph Neural Network Training Using Discrete Geometry

• URL: http://arxiv.org/abs/2207.08026v1
• Date: Sat, 16 Jul 2022 21:50:39 GMT
• Title: Rewiring Networks for Graph Neural Network Training Using Discrete Geometry
• Authors: Jakub Bober, Anthea Monod, Emil Saucan, and Kevin N. Webster
• Abstract summary: Information over-squashing is a problem that significantly impacts the training of graph neural networks (GNNs) In this paper, we investigate the use of discrete analogues of classical geometric notions of curvature to model information flow on networks and rewire them. We show that these classical notions achieve state-of-the-art performance in GNN training accuracy on a variety of real-world network datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Information over-squashing is a phenomenon of inefficient information propagation between distant nodes on networks. It is an important problem that is known to significantly impact the training of graph neural networks (GNNs), as the receptive field of a node grows exponentially. To mitigate this problem, a preprocessing procedure known as rewiring is often applied to the input network. In this paper, we investigate the use of discrete analogues of classical geometric notions of curvature to model information flow on networks and rewire them. We show that these classical notions achieve state-of-the-art performance in GNN training accuracy on a variety of real-world network datasets. Moreover, compared to the current state-of-the-art, these classical notions exhibit a clear advantage in computational runtime by several orders of magnitude.

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