Michael Galkin

Michael Galkin

Graph ML, GNNs, Geometric DL, Knowledge Graphs

Neural Graph Databases

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Published:

A new paper Neural graph reasoning: Complex logical query answering meets graph databases and an accompanying Medium post where we introduce the concept of Neural Graph Databases (NGDB) and a whole new categorization of complex logical query answering tasks. NGDBs answer complex queries right in the latent space and are able to reason over missing links (“what’s missing?”) in addition to standard DB-like retrieval (“what’s there”?).

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2 papers accepted at NeurIPS 2023

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Our team got two papers accepted at the upcoming NeurIPS’23 in New Orleans!

  • A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs. Zhaocheng Zhu, Xinyu Yuan, Mikhail Galkin, Sophie Xhonneux, Ming Zhang, Maxime Gazeau, Jian Tang. preprint
  • Improving Systematic Generalization using Iterated Learning and Simplicial Embeddings. Yi Ren, Samuel Lavoie, Mikhail Galkin, Danica J. Sutherland, Aaron Courville.

Graph Machine Learning @ ICML 2023

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I was lucky to attend ICML’23 in Honolulu in person and see many friends and Graph ML folks. Check my report on latest and greatest graph learning research in the new blog post together with some stunning photos from Hawaii!

Temporal Graph Learning in 2023

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A new Medium post by Andy Huang, Emanuele Rossi, Michael Galkin, and Kellin Pelrine on the recent progress in temporal Graph ML! Featuring theoretical advancements in understanding expressive power of temporal GNNs, discussing evaluation protocols and trustworthiness concerns, looking at temporal KGs, disease modeling, and anomaly detection, as well as pointing to the software libraries and new datasets!

Graph ML in 2023: The State of Affairs

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In a new Medium post, we provide an overview of what’s happened in Graph ML in 2022 and its subfields (and hypothesize for potential breakthroughs in 2023), including Generative Models, Physics, PDEs, Graph Transformerrs, Theory, KGs, Algorithmic Reasoning, Hardware, and more!