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arXiv · 2604.00399

A Cross-graph Tuning-free GNN Prompting Framework

Yaqi Chen·Shixun Huang·Ryan Twemlow·Lei Wang·John Le·Sheng Wang·Willy Susilo·Jun Yan·Jun Shen
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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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Yaqi Chen, Shixun Huang, Ryan Twemlow, Lei Wang, John Le, Sheng Wang, Willy Susilo, Jun Yan, Jun Shen. 2026-04-01. A Cross-graph Tuning-free GNN Prompting Framework. https://arxiv.org/abs/2604.00399

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