4 papers
A Fair Evaluation of Graph Foundation Models for Node Property Prediction
Oleg Platonov, Gleb Bazhenov, Dmitry Eremeev +1
Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attentio…
GraphPFN: A Prior-Data Fitted Graph Foundation Model
Dmitry Eremeev, Oleg Platonov, Gleb Bazhenov +2
Graph foundation models face several fundamental challenges including transferability across diverse domains and data scarcity, which calls into question the very feasibility of cr…
Turning Tabular Foundation Models into Graph Foundation Models
Dmitry Eremeev, Gleb Bazhenov, Oleg Platonov +2
While foundation models have revolutionized fields such as natural language processing and computer vision, their potential in graph machine learning remains largely unexplored. On…
Heat and Matérn Kernels on Matchings
Dmitry Eremeev, Salem Said, Viacheslav Borovitskiy
Applying kernel methods to matchings is challenging due to their discrete, non-Euclidean nature. In this paper, we develop a principled framework for constructing geometric kernels…