A Fair Evaluation of Graph Foundation Models for Node Property Prediction

The paper presents a comprehensive evaluation of nine recent Graph Foundation Models (GFMs) for node property prediction, addressing the lack of standardized benchmarks in the field. Key findings indicate that only the latest GFMs leveraging the Prior-data Fitted Networks paradigm surpass well-tuned Graph Neural Networks (GNNs) in predictive performance, albeit with increased inference costs. This work is crucial for practitioners as it provides a clearer understanding of the trade-offs between GFMs and GNNs, facilitating informed model selection for applications in fraud detection and recommendation systems.