Michael Galkin

Michael Galkin

Graph ML, GNNs, Geometric DL, Knowledge Graphs

Knowledge Graphs @ AAAI 2020

less than 1 minute read

Published:

Here is a fresh digest of KG-related papers in the NLP context from the past AAAI 2020! We’ll check out new stuff in KG-augmented Language Models, GNN-based entity matching, news on link prediction especially in the temporal dimension, and finally some new papers in ConvAI and Knowledge Graph Question Answering!

You May Also Enjoy

2 papers accepted at NeurIPS 2023

Published: | less than 1 minute read

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

Published: | less than 1 minute read

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!

Neural Graph Databases

Published: | less than 1 minute read

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”?).

Temporal Graph Learning in 2023

Published: | less than 1 minute read

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!