Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention
<|reference_start|>Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention: 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.<|reference_end|>
@article{liao2024greener,
    title={Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention},
    author={Tongzhou Liao, Barnab'as P'oczos},
    journal={arXiv preprint arXiv:2407.05649},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05649},
    primaryClass={cs.LG cs.AI cs.NE}
}