A cross-graph tuning-free GNN prompting framework.

Authors

Yaqi Chen, Shixun Huang, Lei Wang, Ryan Twemlow, John Le, Sheng Wang, Willy Susilo, Jun Yan, Jun Shen

Published in

Neural networks : the official journal of the International Neural Network Society. Volume 205. Issue Pt B. Pages 109466. Aug 10, 2026. Epub Aug 10, 2026.

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.

PMID:
42574820
Bibliographic data and abstract were imported from PubMed on 11 Aug 2026.

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