Post #873

GraphML News (Nov 16th) - ICLR 2025 Stats, Official AlphaFold 3 and RFam, Faster MD 📊 PaperCopilot comprised the basic statistics of ICLR 2025 initial reviews and best scored papers - a few GNN papers are in top-100 and we’ll keep an eye on them! Meanwhile, Azmine Toushik Wasi compiled a list of accepted graph papers at NeurIPS 2024 grouped by categories - from GNN theory to generative models to transformers to OOD generalization and many more. 🧬 Google DeepMind finally released the official code for AlphaFold 3 featuring a monstrous 3k lines of code Structure class and some kernels implemented in Triton (supporting the fact that Triton is the future and you should implement your most expensive neural net ops as efficient kernelized ops). Somewhat orthogonally to AF3, the Baker Lab presented RFam, the improved version of RFdiffusion, in a paper about metallohydrolases. RFam now uses flow matching (welcome on board), allows for scaffolding arbitrary atom-level motifs and sequence-position-agnostic scaffolding. Waiting for the code soon! 🏎️ Microsoft Research announced AI2BMD - a freshly accepted to Nature method for accelerating ab-initio molecular dynamics of proteins with equivariant GNNs (based on VisNet, already in PyG) scaling it up to impressive 10k atoms in a structure (far beyond the capabilities of standard MD tools). Besides, the authors collected a new dataset of 20M DFT-computed snapshots which would be of great help to the MD community. 🌊 Continuing the simulation note, NXAI and JKU Linz presented NeuralDEM, a neural approach to replace Discrete Element Method (DEM) in complex physical simulations (like fluid dynamics) with transformers and neural operators. NeuralDEM is as accurate and stable as vanilla DEM while being much faster and allowing for longer sim times. Weekend reading: Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs by Levi Rauchwerger, Stefanie Jegelka, and Ron Levie - it is known that the vanilla WL test assumes no node features. This is one of the first works to study GNN properties on featurized graphs. GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts by Shirley Wu et al feat. Bruno Ribeiro and Jure Leskovec Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning by Haitz Borde et al feat. Michael Bronstein - a transformer-like block where multi-head attention is replaced with a GCN (keeping layer norms, residual stream and MLPs intact) is suprisingly competitive on very large graphs