GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning
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, ne...
| Authors | Yan Jiang, Ruihong Qiu, Zi Huang |
|---|---|
| Primary category | cs.LG |
| Published | 14 May 2026 |
| Last updated at source | 26 May 2026 |
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