arXiv:2605.14809v2 Announce Type: replace 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 rest
The continuous development and refinement of Graph Foundation Models necessitate specialized techniques like prompt tuning to enhance their adaptability and performance across diverse tasks and domains.
This development addresses a key limitation in current Graph Foundation Models by improving their adaptability and reducing reliance on source-domain specific information, which is critical for their broader application and efficiency.
Graph Foundation Models (GFMs) may become more versatile and efficient by decoupling prompt engineering from specific pre-training strategies, enabling better generalization and reducing the cost of fine-tuning for new applications.
- · AI/ML researchers
- · Graph AI developers
- · Enterprises using GFM-powered applications
- · Data scientists
- · Methods heavily reliant on source-domain specific prompt entanglement
- · Less adaptable graph-learning approaches
Improved performance and broader applicability of Graph Foundation Models in various fields.
Accelerated development of AI agents and complex systems leveraging graph structures for decision-making and knowledge representation.
Enhanced operational efficiency and novel applications across industries due to more robust and flexible graph-based AI solutions.
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