8 papers
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…
Metropolis-Scale Road Network Datasets for Fine-Grained Urban Traffic Modeling
Fedor Velikonivtsev, Oleg Platonov, Ekaterina Alimaskina +2
Modeling traffic dynamics is a critical challenge for urban computing, with applications from real-time traffic management to infrastructure planning. However, progress in this are…
GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data
Gleb Bazhenov, Oleg Platonov, Liudmila Prokhorenkova
Although data that can be naturally represented as graphs is widespread in real-world applications across diverse industries, popular graph ML benchmarks for node property predicti…
Measuring Diversity: Axioms and Challenges
Mikhail Mironov, Liudmila Prokhorenkova
This paper addresses the problem of quantifying diversity for a set of objects. First, we conduct a systematic review of existing diversity measures and explore their undesirable b…
Revisiting Graph Homophily Measures
Mikhail Mironov, Liudmila Prokhorenkova
Homophily is a graph property describing the tendency of edges to connect similar nodes. There are several measures used for assessing homophily but all are known to have certain d…