Post #760

GraphML News GPT-4 made the graph community scratching their heads as well (maybe not as much as academic NLP researchers) - look at the molecule search example at the very end of the technical report. Andrew White was among the few researchers working on this example, he compiled a thread how GPT-4 empowered with external tools can do a very impressive job proposing new molecules. Minkai Xu delivered a lecture “Geometric Graph Learning From Representation to Generation” as a part of the cs224w ML with Graphs course at Stanford (perhaps the most famous class about Graph ML). The lecture covers the basics of invariant and equivariant GNNs and introduces GeoDiff, a diffusion model for generating 3D molecules. Slides of the whole Winter’23 course are now available. Weekend reading: The Descriptive Complexity of Graph Neural Networks - a massive 88-pager from Martin Grohe proving that GNNs fall into the TC0 complexity class. This is a potential breakthrough since many database query languages fall into AC0 and TC0. Zero-One Laws of Graph Neural Networks by Adam-Day et al. - shows an interesting result that GCN-like MPNNs with random features map final graph representations to zeros or ones with the growing size of graphs. GATs and GINs are not (yet) prone to this behavior. Allegro-Legato: Scalable, Fast, and Robust Neural-Network Quantum Molecular Dynamics via Sharpness-Aware Minimization by Ibayashi et al - an improved version of Allegro, current SOTA in molecular dynamics simulations, with faster convergence and better stability.