This will be a 30-60 minute presentation covering our ongoing work to generalize graph learning models across tasks. We’ll provide an overview of Graph Foundation Models (GFMs), defining them as single models designed to learn transferable representations for generalization across diverse graphs and tasks, contrasting them with traditional graph learning approaches. Then we’ll discuss the motivation for GFMs advocating for the need for transferable learning and generalization. We’ll highlight successful GFM examples in link prediction and node classification, while also acknowledging open challenges such as feature heterogeneity and task generalization. Finally, we’ll briefly explore the intersection of GFMs and Large Language Models (LLMs), including text-space approaches and enhancing LLM reasoning with graph structures.

We expect that this Expo will be of broad interest to ICML attendees.

All presenters are experts currently working in this area.

Bryan Perozzi has been working on graph machine learning for 10+ years, and has 20000+ citations in the area.
Michael Galkin has 2800 citations and an h-index of 28.