7 papers · 1 filter
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina PetroviÄ, 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…
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Ben Finkelshtein, Ä°smail Ä°lkan 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…
Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models
Adrian Hayler, Xingyue Huang, Ä°smail Ä°lkan 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…
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…
Link Prediction with Relational Hypergraphs
Xingyue Huang, Miguel Romero Orth, Pablo Barceló +2
Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful appli…
Fully-inductive Node Classification on Arbitrary Graphs
Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin +3
One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new struct…