这篇论文帮你打破了GNN和GFM的迷雾：实测9个模型，发现只有Prior-data Fitted Networks那类才真能打，但代价是推理更慢。想搞清楚哪些图模型值得用就看它。
该研究对9种最新的图基础模型（GFM）在节点属性预测任务上进行了重新评估，并与强基线图神经网络（GNN）比较。只有基于Prior-data Fitted Networks范式的最新型GFM在预测性能上超越调优后的GNN，但推理成本更高。不同论文因评测设置差异导致结果难以直接对比，本研究提供了统一的公平比较框架。
A Fair Evaluation of Graph Foundation Models for Node Property Prediction
Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attention. While many different types of models are called GFMs, particular interest has been paid to GFMs designed for node property prediction tasks, which is one of the most popular settings in Graph ML with lots of real-world applications from fraud detection in financial and social networks to recommendation systems for e-commerce and user-generated content platforms. While a number of GFMs for this task have been recently proposed, the field has not converged to a unified evaluation setting, and different works evaluate their models in widely different ways, preventing reliable comparison of GFMs with each other and with other types of models. In this work, we conduct a fair and rigorous reevaluation of 9 recent GFMs for node property prediction, comparing them to strong Graph Neural Network (GNN) baselines. We find that, among these GFMs, only the most recent ones based on the Prior-data Fitted Networks paradigm outperform well-tuned GNNs in predictive performance, although at a higher inference cost.