4 citations · 10 across the 17 of their papers we have counts for
6 papers · 2 filters
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina Petrović, Lazar Atanackovic, Viggo Moro +5
Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Such models rely on key assumptions about t…
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…
Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak +4
We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relations. Knowledge graph foundation m…
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein +1
Graph machine learning architectures are typically tailored to specific tasks on specific datasets, which hinders their broader applicability. This has led to a new quest in graph…
HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs
Xingyue Huang, Mikhail Galkin, Michael M. Bronstein +1
Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., nodes unseen during training). Existing…
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…