GraphFM: A Comprehensive Benchmark for Graph Foundation Model
Yuhao Xu, Xinqi Liu, Keyu Duan, Yi Fang, Yu-Neng Chuang, Daochen Zha,, Qiaoyu Tan

TL;DR
This paper introduces GraphFM, a comprehensive benchmark for evaluating the generalization, scalability, and efficiency of Graph Foundation Models, providing insights to guide future research in graph self-supervised learning.
Contribution
It presents a rigorous benchmark for analyzing and comparing graph self-supervised models across multiple aspects including generalization, scalability, and training efficiency.
Findings
Self-supervised GNN models vary in generalization performance across tasks.
Scalability differs between full-batch and mini-batch training strategies.
Training efficiency metrics highlight the resource demands of different models.
Abstract
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream tasks. Despite extensive research into self-supervised learning as the cornerstone of FMs, several outstanding issues persist in Graph Foundation Models that rely on graph self-supervised learning, namely: 1) Homogenization. The extent of generalization capability on downstream tasks remains unclear. 2) Scalability. It is unknown how effectively these models can scale to large datasets. 3) Efficiency. The training time and memory usage of these models require evaluation. 4) Training Stop Criteria. Determining the optimal stopping strategy for pre-training across multiple tasks to maximize performance on downstream tasks. To address these questions, we have constructed a rigorous benchmark that thoroughly…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsModel-Driven Software Engineering Techniques · Advanced Graph Neural Networks · Graph Theory and Algorithms
MethodsGraph Neural Network
