Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
<|reference_start|>Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks: Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic nature of the graph structure, resulting in sub-optimal node and graph representations. To address this limitation, we propose a novel Graph structure Prompt Learning method (GPL) to enhance the training of GNNs, which is inspired by prompt mechanisms in natural language processing. GPL employs task-independent graph structure losses to encourage GNNs to learn intrinsic graph characteristics while simultaneously solving downstream tasks, producing higher-quality node and graph representations. In extensive experiments on eleven real-world datasets, after being trained by GPL, GNNs significantly outperform their original performance on node classification, graph classification, and edge prediction tasks (up to 10.28%, 16.5%, and 24.15%, respectively). By allowing GNNs to capture the inherent structural prompts of graphs in GPL, they can alleviate the issue of over-smooth and achieve new state-of-the-art performances, which introduces a novel and effective direction for GNN research with potential applications in various domains.<|reference_end|>
@article{huang2024graph,
    title={Graph Structure Prompt Learning: A Novel Methodology to Improve
  Performance of Graph Neural Networks},
    author={Zhenhua Huang, Kunhao Li, Shaojie Wang, Zhaohong Jia, Wentao Zhu,
  Sharad Mehrotra},
    journal={arXiv preprint arXiv:2407.11361},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11361},
    primaryClass={cs.LG cs.SI}
}