Feature Transportation Improves Graph Neural Networks

• URL: http://arxiv.org/abs/2307.16092v2
• Date: Wed, 20 Dec 2023 07:43:53 GMT
• Title: Feature Transportation Improves Graph Neural Networks
• Authors: Moshe Eliasof, Eldad Haber, Eran Treister
• Abstract summary: Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data. In this paper, we propose a novel GNN architecture inspired by Advection-Diffusion-Reaction systems, called ADR-GNN.
• Score: 15.919986945096182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data. However, GNNs still face challenges in modeling complex phenomena that involve feature transportation. In this paper, we propose a novel GNN architecture inspired by Advection-Diffusion-Reaction systems, called ADR-GNN. Advection models feature transportation, while diffusion captures the local smoothing of features, and reaction represents the non-linear transformation between feature channels. We provide an analysis of the qualitative behavior of ADR-GNN, that shows the benefit of combining advection, diffusion, and reaction. To demonstrate its efficacy, we evaluate ADR-GNN on real-world node classification and spatio-temporal datasets, and show that it improves or offers competitive performance compared to state-of-the-art networks.

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