Michael Galkin

Michael Galkin

Graph ML, GNNs, Geometric DL, Knowledge Graphs

One Paper Accepted @ LoG 2022

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We got one paper accepted at the inaugural Learning on Graphs (LoG) 2022 conference!

  • Pablo Barcelo, Mikhail Galkin, Christopher Morris, Miguel Romero Orth. Weisfeiler and Leman Go Relational: we introduce a Relational WL test to quantify expressiveness of relational GNNs (R-GCN and CompGCN).

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Graph Learning Tutorial @ ICML 2024

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Participated (online) in the panel discussion with Bryan Perozzi, Michael Bronstein, and Christopher Morris on graph foundation models held during the Graph Learning Tutorial at ICML 2024, thanks Ameya and Adrian for inviting!

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In our new Medium blogpost with Michael Bronstein, Jianan Zhao, Haitao Mao, and Zhaocheng Zhu we discuss foundation models in Graph & Geometric DL: from the core theoretical and data challenges to the most recent models that you can try already today!