Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention
Keywords: Graph Neural Networks, Graph Encoding, Graph Rewiring, Attention Mechanism, Deep Learning
TL;DR: We propose a novel GNN that achieves SOTA performance with random walk encoding, random rewiring, and a novel additive attention mechanism.
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% improvement in ZINC MAE.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 12813
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