**Graph ML News (May 6th)** ICLR’23 has finished this week, to those who travelled to Kigali - have a safe trip back ![🙂](https://web.telegram.org/k/#@graphMLassets/img/emoji/1f642.png) 

Meanwhile, you might have missed the [ICLR Blogposts Track](https://iclr-blogposts.github.io/2023/about) - 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](https://iclr-blogposts.github.io/2023/blog/2023/sets-and-graphs/) (by Fabian B. Fuchs and Petar Veličković**)**, on [Neural PDE Solvers](https://iclr-blogposts.github.io/2023/blog/2023/autoregressive-neural-pde-solver/) (by Yolanne Lee), and [Thinking Like Transformer](https://iclr-blogposts.github.io/2023/blog/2023/raspy/) (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](https://sites.google.com/view/learning-on-graph-paris-meetup/home) 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](https://sites.google.com/view/limboecmlpkdd2023/home). 

If you fancy Lisboa in September, you might want to submit to the [Special Track on AI on Networks for Social Good](https://sites.google.com/view/ain4go23/home), part of the ACM Conference on Information Technology for Social Good. Thanks to Manuel Dileo for the pointers ![👏](https://web.telegram.org/k/#@graphMLassets/img/emoji/1f44f.png) 

For the weekend reading, have a look at: [Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://arxiv.org/abs/2304.09433) by Simran Arora and Christopher Ré’s lab

[When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle on Node Distinguishability](https://arxiv.org/abs/2304.14274) by Sitao Luan feat. Jure Leskovec and Doina Precup 

[An Exploration of Conditioning Methods in Graph Neural Networks](https://arxiv.org/abs/2305.01933) by Yeskendir Koishekenov and Erik J. Bekkers