Post #888

GraphML News (March 2nd) - GNNs at SnapChat, Lab in the Loop, Flow matching in scRNA 📸 SnapChat released a paper about GiGL - Gigantic Graph Learning library - together with the success story of recsys GNN use-cases at Snapchat powering 30+ launches and spanning applications from friend recommendations to fraud and abuse detection. GiGL adopts the graph sampling strategy around target nodes and scales to internal graphs of ~900M nodes, 16.8B edges, and dozens of node/edge features. Some interesting technical details: most of the performance improvements is brought by feature cleanup and engineering (+20 MRR points) while engineering GATv2 with more layers and neighboring nodes brings about +10 MRR points. Some lessons learned and insights: increasing off-line metrics hurts online metrics, graph sparsification is more beneficial than densification, shallow graph embeddings can sometimes be useful, too. More technical details are in the paper, the code is also published Congrats to SnapChat on joining the elite group of GNN connoisseurs together with Pinterest, Google Maps, Amazon, Spotify, LinkedIn, Alibaba, and many others 🧬 Genentech released a massive paper describing the lab-in-the-loop process for antibody design with deep learning. Targeting clinically relevant antigens, the lab-in-the-loop includes all the recent Genentech papers including those on generative models (like recent discrete walk-jump sampling) for candidate generation and property optimization (eg, Lambo-2), property predictors (protein LMs), filtering and ranking, and experimental (in vivo) validation on 🐀. Experimentally, the active loop pipeline produced 10 candidates per target with 3-100x better binding. ✍️ Finally, Karin Hrovatin wrote an overview blog post of recent uses of flow matching models in single-cell gene expression data (scRNA) covering CFGen, Wasserstein Flow Matching, and Meta Flow Matching.