Michael GalkininTowards Data ScienceFoundation Models in Graph & Geometric Deep LearningIn this post, we argue that the era of Graph FMs has already begun and provide a few examples of how one can use them already today.Jun 186Jun 186

Michael GalkininTowards Data ScienceGraph & Geometric ML in 2024: Where We Are and What’s Next (Part II — Applications)Trends and recent advancements in Graph and Geometric Deep LearningJan 165Jan 165

Michael GalkininTowards Data ScienceGraph & Geometric ML in 2024: Where We Are and What’s Next (Part I — Theory & Architectures)Trends and recent advancements in Graph and Geometric Deep LearningJan 162Jan 162

Michael GalkininTowards Data ScienceULTRA: Foundation Models for Knowledge Graph ReasoningOne model to rule them allNov 3, 20235Nov 3, 20235

Michael GalkininTowards Data ScienceGraph Machine Learning @ ICML 2023Recent advancements and hot trends, August 2023 editionAug 6, 20235Aug 6, 20235

Michael GalkininTowards Data ScienceNeural Graph DatabasesA new milestone in graph data managementMar 28, 20233Mar 28, 20233

Michael GalkininTowards Data ScienceGraph ML in 2023: The State of AffairsHot trends and major advancementsJan 1, 20233Jan 1, 20233

Michael GalkininTowards Data ScienceDenoising Diffusion Generative Models in Graph MLIs Denoising Diffusion all you need?Nov 26, 2022Nov 26, 2022

Michael GalkininTowards Data ScienceGraph Machine Learning @ ICML 2022Recent advancements and hot trends, July 2022 editionJul 25, 20224Jul 25, 20224

Michael GalkininTowards Data ScienceGraphGPS: Navigating Graph TransformersRecipes for cooking the best graph transformersJun 14, 20224Jun 14, 20224