22 citations · 27 across the 8 of their papers we have counts for
8 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…
Cluster Attention for Graph Machine Learning
Oleg Platonov, Liudmila Prokhorenkova
Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message p…
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…
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…
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…
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
Oleg Platonov, Denis Kuznedelev, Michael Diskin +2
Node classification is a classical graph machine learning task on which Graph Neural Networks (GNNs) have recently achieved strong results. However, it is often believed that stand…
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
Oleg Platonov, Denis Kuznedelev, Artem Babenko +1
Homophily is a graph property describing the tendency of edges to connect similar nodes; the opposite is called heterophily. It is often believed that heterophilous graphs are chal…