GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning

• URL: http://arxiv.org/abs/2605.14809v1
• Date: Thu, 14 May 2026 13:22:33 GMT
• Title: GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning
• Abstract summary: Recent research has extended graph prompts to improve Graph Foundation Models (GFMs)<n>Most existing methods embed source-domain information into prompts, which serve either as input to GFMs or encoded during model pre-training.<n>Motivated by these insights, this paper aims to empower GFMs with pre-training-agnostic test-time graph prompt tuning, named GFMate.
• Score: 41.859993506122194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph prompt tuning has shown great potential in graph learning by introducing trainable prompts to enhance the model performance in conventional single-domain scenarios. Recent research has extended graph prompts to improve Graph Foundation Models (GFMs) by few-shot tuning auxiliary prompts. Despite their progress, most existing methods embed source-domain information into prompts, which serve either as input to GFMs or encoded during model pre-training. Such prompt entanglement with specific source domains and GFM pre-training strategy restricts their generalisability to other domains and different GFMs. Furthermore, existing GFM prompts merely rely on few-shot tuning for adaptation, neglecting the rich information in unlabelled target domain test data. Motivated by these insights, this paper aims to empower GFMs with pre-training-agnostic test-time graph prompt tuning, named GFMate. GFMate introduces centroid and layer prompts applied after pre-training on target domains, avoiding entanglement with specific source domains and model pre-training. In addition, a test-time complementary learning objective is devised to exploit both labelled and unlabelled target domain data for effective test-time prompt tuning. Extensive experiments on 12 benchmark datasets demonstrate the superior performance and efficiency of GFMate, achieving improvements of up to 30.63%. Code is available at https://github.com/YanJiangJerry/GFMate.

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