3 papers
cs.LG2025
Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models
Adrian Hayler, Xingyue Huang, İsmail İlkan Ceylan +2
Graph foundation models (GFMs) have recently emerged as a promising paradigm for achieving broad generalization across various graph data. However, existing GFMs are often trained…
cs.LG2025
How Expressive are Knowledge Graph Foundation Models?
Xingyue Huang, Pablo Barceló, Michael M. Bronstein +4
Knowledge Graph Foundation Models (KGFMs) are at the frontier for deep learning on knowledge graphs (KGs), as they can generalize to completely novel knowledge graphs with differen…
cs.LG2024
One Model, Any Conjunctive Query: Graph Neural Networks for Answering Queries over Incomplete Knowledge Graphs
Krzysztof Olejniczak, Xingyue Huang, Mikhail Galkin +1
Motivated by the incompleteness of modern knowledge graphs, a new setup for query answering has emerged, where the goal is to predict answers that do not necessarily appear in the…