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GPL_Dataset
- Citation Author(s):
- Submitted by:
- Kunhao Li
- Last updated:
- Fri, 08/09/2024 - 10:46
- DOI:
- 10.21227/e85y-3q38
- License:
- Categories:
- Keywords:
Abstract
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 \textbf{G}raph structure \textbf{P}rompt \textbf{L}earning 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.
GPL的脚本和数据