Friday News: LOG Accepted Papers, NeurIPS

The inaugural event of the Learning of Graphs (LOG) conference announced accepted papers, extended abstracts, and spotlights - the acceptance rate this year is pretty tough (<25%) but we have heard multiple times that the quality of reviews is on average higher than in other big conferences. Is it the impact of the $$$ rewards for the best reviewers?

Tech companies summarize their presence at NeurIPS’22 that starts next week: have a look at works from DeepMind, Amazon, Microsoft, and the GraphML team from Google.

A new blog post by Petar Veličković and Fabian Fuchs on universality of neural networks on sets and graphs - the authors identify a direct link between permutation-invariant DeepSets and permutation-invariant aggregations in GNNs like GIN. However, when it comes to multisets (such as nodes sending exactly the same message), PNA might be more expressive thanks to the link to the theoretical findings - given a set of n elements, that the width of the encoder should be at least n - recall that PNA postulates that it is necessary to have n aggregators. Nice read with references!