Post #856

GraphML News (August 3rd) - NeurIPS workshops, MoML @ MIT, RUM and GraM ⛷️ NeurIPS’24 announced 56 accepted workshops (brace yourself, Vancouver convention center). In addition to a good bunch of LLM, VLM, and foundation model-focused events, graph and geometric learning folks might be interested in: - AI for New Drug Modalities - Machine Learning in Structural Biology - Symmetry and Geometry in Neural Representations - Multimodal Algorithmic Reasoning - Machine Learning and the Physical Sciences - AI for Accelerated Materials Design 🧬 The second part of MoML 2024 (Molecular ML) will be happening at MIT on November 5, you can submit short papers until October 10th. The authors of accepted papers get free admission! 💎 The GraM workshop of ICML’24 published accepted blogposts with some hidden gems like JAX implementation of EGNN, intro to equivariant neural fields, and the study of how consistency models don’t work for 3D molecule generation. Check out others as well - most of them require only entry-level background. 📈Non-convolutional Graph Neural Networks by Yuanqing Wang and Kyunghyun Cho (the OG of GRUs) introduce RUM (random walk with unified memory) nets free of convolutions. Practically, the recipe of RUM included sampling random walks with anonymous node ID sequences (tracking the first occurrence of a node ID in the sequence), encodes both sequences via RNNs (sure, you can drop-in your fav Mamba here), concats both vectors with an MLP on top. The authors show RUMs are more expressive than 1-WL GNNs while not suffering from oversmoothing and oversquashing (and beating the baselines on a bunch of benchmarks). Interestingly, RUMs look like DeepWalk on steroids with several improvements. Is Bryan Perozzi the Noam Shazeer of graph learning? 🤔 More weekend reading: Spatio-Spectral Graph Neural Networks by Simon Geisler et al feat. Stephan Günnemann - spectral GNNs can be strong performers, too - just to contrast with RUMs Learning production functions for supply chains with graph neural networks by Serina Chang et al feat Jure Leskovec - a cool work that frames supply chains as temporal graphs, shows significant gains in prediction accuracy, and releases the data simulator What Are Good Positional Encodings for Directed Graphs? by Yinan Huang, Haoyu Wang, and Pan Li. The answer is the Magnetic Laplacian with multiple potential factors (multi-q) - your best choice for DAGs.