Can Graph Foundation Models Generalize Over Architecture?

1. 1Identify the limitations of current graph foundation models in terms of their generalizability across architectures
2. 2Investigate the range of effective GNN architectures that can be used with GFMs
3. 3Develop new methods to improve the generalizability of GFMs across different architectures and domains

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

💡 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

🤖 Graph foundation models can generalize, but with limitations 📈