Graph ML News (May 6th)

ICLR’23 has finished this week, to those who travelled to Kigali - have a safe trip back 🙂

Meanwhile, you might have missed the ICLR Blogposts Track - a collection of insightful articles for which it is often more handy to express the content as a blog post rather than a full paper. Particularly interesting are On Universality of Neural Networks on Sets vs Graphs (by Fabian B. Fuchs and Petar Veličković), on Neural PDE Solvers (by Yolanne Lee), and Thinking Like Transformer (by Alexander Rush, Gail Weiss). I would generally recommend submitting there (my post was accepted at ICLR’22 Blog Post Track) - it was a pleasant experience and you also do some community serving writing about your research.

A few upcoming events:

LoG Paris Meetup on June 14th in Paris at CentraleSupélec, Université Paris-Saclay with the keynote from Michael Bronstein.

Michael is going to be one of the keynote speakers at ECML PKDD 2023 in September in Torino - the list of accepted workshops should appear soon, so far we know about the Workshop on Learning and Mining with Blockchains. If you fancy Lisboa in September, you might want to submit to the Special Track on AI on Networks for Social Good, part of the ACM Conference on Information Technology for Social Good. Thanks to Manuel Dileo for the pointers 👏

For the weekend reading, have a look at:

Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes by Simran Arora and Christopher Ré’s lab

When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle on Node Distinguishability by Sitao Luan feat. Jure Leskovec and Doina Precup

An Exploration of Conditioning Methods in Graph Neural Networks by Yeskendir Koishekenov and Erik J. Bekkers