A Cross-graph Tuning-free GNN Prompting Framework

• URL: http://arxiv.org/abs/2604.00399v1
• Date: Wed, 01 Apr 2026 02:34:23 GMT
• Title: A Cross-graph Tuning-free GNN Prompting Framework
• Abstract summary: We introduce a Cross-graph Tuning-free Prompting Framework (CTP), which supports both homogeneous and heterogeneous graphs.<n>CTP can be directly deployed to unseen graphs without further parameter tuning, and thus enables a plug-and-play GNN inference engine.<n>Experiments on few-shot prediction tasks show that, compared to SOTAs, achieves an average accuracy gain of 30.8% and a maximum gain of 54%.
• Score: 22.062254916602637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: GNN prompting aims to adapt models across tasks and graphs without requiring extensive retraining. However, most existing graph prompt methods still require task-specific parameter updates and face the issue of generalizing across graphs, limiting their performance and undermining the core promise of prompting. In this work, we introduce a Cross-graph Tuning-free Prompting Framework (CTP), which supports both homogeneous and heterogeneous graphs, can be directly deployed to unseen graphs without further parameter tuning, and thus enables a plug-and-play GNN inference engine. Extensive experiments on few-shot prediction tasks show that, compared to SOTAs, CTP achieves an average accuracy gain of 30.8% and a maximum gain of 54%, confirming its effectiveness and offering a new perspective on graph prompt learning.

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