GraphML News (Oct 3rd)

Well, no big news from the past weekend since ICLR’24 submissions are still not available after the main deadline 🙁 At least we can read the abstracts of all accepted NeurIPS’23 papers here. A brief search indicates that the amount of papers with “diffusion” (192) is as large as “graph” papers (202).

Meanwhile, VantAI launches a monthly lecture series on Generative AI in Drug Discovery hosted by Michael Bronstein and Bruno Correia. The inaugural meeting will be held this Friday, October 6, at 11 am ET / 5 pm CET. Free to join using the links provided.

A few fresh software releases: PyDGN got updated to 1.5, and industry-grade GraphStorm released v0.2 featuring better support for distributed training on GPUs.

Paper reading:

Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems (NeurIPS’23) by Google on featurization strategies for ML in search, ads, and recsys.

Limits, approximation and size transferability for GNNs on sparse graphs via graphops (NeurIPS’23) by Thien Le and Stefanie Jegelka on size generalization in GNNs.

Sheaf Hypergraph Networks (NeurIPS’23) by Iulia Duta et al (math alert 🤯)

On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters by Matthias Lanzinger and Pablo Barceló