GraphML News (Oct 26th) - LOG meetups, Orbital round, ESANN 2025

🍻 The Learning of Graphs conference continues to update the list of local meetups - the networks already includes 13 places from well-known graph learning places like Stanford, NYC, Paris, Oxford, Aachen, Amsterdam, Tel Aviv down to Tromsø, Uppsala, Siena, New Delhi, Suzhou, and Vancouver (Late November in Tromsø, talking graphs with a cup of glühwein and snow outside must be a quite a cozy venue). The call for meetups is still open!

On this note, the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2025) will host 3 special sessions on graph learning: Foundation and Generative Models for Graphs, Graph Generation for Life Sciences, and Network Science Meets AI. Submission deadline is November 20, 6 pages tops. Thanks to Manuel Dileo for the pointer! ESANN 2025 will take place on April 23-25 (2025) in Bruges (jokes about the movie and Tottenham are welcome).

💸 Orbital Materials secured a new funding round led by NVIDIA Ventures (financial details undisclosed) – timed nicely coinciding with the recent release of the ML potential GNN Orb-v2. A new unicorn from AI 4 Science is coming? 🤔

Weekend reading:

Learning Graph Quantized Tokenizers for Transformers by Limei Wang, Kaveh Hassani et al and Meta - an unorthodox approach for graph tokenization via vector quantization and codebook learning, conceptually similar to VQ-GNNs (NeurIPS 2021), strange to not see this older paper cited

Relaxed Equivariance via Multitask Learning by Ahmed Elhag et al feat Michael Bronstein - instead of baking equivariances right into models, let’s add it as a loss component and allow a model to learn and use as much equivariance as necessary, brings 10x inference speedups.

Homomorphism Counts as Structural Encodings for Graph Learning by Linus Bao, Emily Jin, et al - introduces motif structural encoding (MoSE) for graph transformers. Paired with GraphGPS, brings MAE on ZINC from 0.07 down to 0.062 and to 0.056 with GRIT.