Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention

• URL: http://arxiv.org/abs/2407.05649v3
• Date: Wed, 9 Oct 2024 16:32:11 GMT
• Title: Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention
• Authors: Tongzhou Liao, Barnabás Póczos,
• Abstract summary: We introduce Graph Attention with Structures (GRASS), a novel GNN architecture, to enhance graph relative attention. GRASS rewires the input graph by superimposing a random regular graph to achieve long-range information propagation. It also employs a novel additive attention mechanism tailored for graph-structured data.
• Score: 12.409982249220812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Networks (GNNs) have become important tools for machine learning on graph-structured data. In this paper, we explore the synergistic combination of graph encoding, graph rewiring, and graph attention, by introducing Graph Attention with Stochastic Structures (GRASS), a novel GNN architecture. GRASS utilizes relative random walk probabilities (RRWP) encoding and a novel decomposed variant (D-RRWP) to efficiently capture structural information. It rewires the input graph by superimposing a random regular graph to enhance long-range information propagation. It also employs a novel additive attention mechanism tailored for graph-structured data. Our empirical evaluations demonstrate that GRASS achieves state-of-the-art performance on multiple benchmark datasets, including a 20.3% reduction in mean absolute error on the ZINC dataset.

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