Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks

• URL: http://arxiv.org/abs/2407.11361v1
• Date: Tue, 16 Jul 2024 03:59:18 GMT
• Title: Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
• Authors: Zhenhua Huang, Kunhao Li, Shaojie Wang, Zhaohong Jia, Wentao Zhu, Sharad Mehrotra,
• Abstract summary: We propose a novel Graph structure Prompt Learning method (GPL) to enhance the training of Graph networks (GNNs) GPL employs task-independent graph structure losses to encourage GNNs to learn intrinsic graph characteristics while simultaneously solving downstream tasks. In experiments on eleven real-world datasets, after being trained by neural prediction, GNNs significantly outperform their original performance on node classification, graph classification, and edge tasks.
• Score: 13.655670509818144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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