arXiv:2605.29828v1 Announce Type: new Abstract: Graph foundation models (GFMs) aim to reuse a single backbone across diverse graph domains, yet their transfer is often uneven and can exhibit negative transfer. While most prior work improves transfer through architectural or adaptation choices, we ask a data-centric question: which properties of two graph domains determine how much a fixed representation model changes its outputs? Using a graphon-based continuous limit for dense graphs, we show that for both set-based and message-passing tokenizations, any Lipschitz backbone admits an explicit
This paper addresses a fundamental challenge in applying foundation models to graph data, a domain with increasing importance across many applications.
Improving the transferability and understanding the robustness of Graph Foundation Models (GFMs) is crucial for their practical implementation and reliability across diverse real-world problems.
This data-centric theory provides a new lens for understanding GFM transfer, which could lead to more robust and generalizable AI applications in complex graph environments.
- · AI researchers
- · Graph AI developers
- · Data scientists in complex networks
- · Industries relying on graph data analysis
- · Developers of domain-specific graph models
- · Companies with highly specialized graph datasets
This research provides theoretical underpinnings for better designing and evaluating Graph Foundation Models.
It could accelerate the development of more universal and adaptable AI systems for network analysis across various scientific and commercial applications.
Generalized and robust GFMs might enable new forms of AI-driven insights and automation in areas currently too complex for existing models, impacting drug discovery, supply chain optimization, and national security.
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Read at arXiv cs.LG