Can Graph Foundation Models Generalize Over Architecture?

📰 ArXiv cs.AI

Graph foundation models can generalize over architecture, but existing work has limitations in terms of the range of effective GNN architectures

advanced Published 25 Mar 2026

Action Steps

Identify the limitations of current graph foundation models in terms of their generalizability across architectures
Investigate the range of effective GNN architectures that can be used with GFMs
Develop new methods to improve the generalizability of GFMs across different architectures and domains

Who Needs to Know This

Researchers and engineers working on graph neural networks and foundation models can benefit from understanding the generalizability of GFMs across different architectures, as it can inform the development of more robust and widely applicable models

Key Insight

💡 Graph foundation models can generalize zero-shot across graphs of arbitrary scales, feature dimensions, and domains, but current work is limited by a narrow set of effective GNN architectures

Key Takeaways

Graph foundation models can generalize over architecture, but existing work has limitations in terms of the range of effective GNN architectures

Full Article

Title: Can Graph Foundation Models Generalize Over Architecture?

Abstract:
arXiv:2603.22984v1 Announce Type: cross Abstract: Graph foundation models (GFMs) have recently attracted interest due to the promise of graph neural network (GNN) architectures that generalize zero-shot across graphs of arbitrary scales, feature dimensions, and domains. While existing work has demonstrated this ability empirically across diverse real-world benchmarks, these tasks share a crucial hidden limitation: they admit a narrow set of effective GNN architectures. In particular, current dom

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