Post #880

GraphML News (Dec 20th) - Thoughts after NeurIPS, ICLR workshops, new blogposts In the last op-ed of this year and returning back from NeurIPS, it’s about time to reflect on the state of graph learning research. Comments to this post should be open (hopefully?) 🤔 At the age when o3 solves some of incredibly hard FrontierMath problems, when NotebookLM allows to call into a podcast generated about a paper of your choice, and Veo2 generates 4K videos with increasingly correct physics, it is somewhat frustrating to see that the graph learning (meaning vanilla 2D graph learning here, because geometric DL is flourishing in the AI 4 Science areas) community is still obsessed with WL tests, node classification on OGB, and other toy tasks that increasingly lose relevance in the modern deep learning world. Is it the issue of too toyish benchmarks, the lack of cool applications, or something else? Is Graph ML to be confined in the recsys and retail predictions domain or it could get its own “RLHF revival moment”? 🏗️ ICLR 2025 started announcing the accepted workshops, you might find some of those interesting: - Frontiers in Probabilistic Inference: Sampling Meets Learning - Generative and Experimental Perspectives for Biomolecular Design - Weight Space Learning - Learning Meaningful Representations of Life - AI for Accelerated Materials Design will be back, too 📝 New blogposts! Understanding Transformer reasoning capabilities via graph algorithms by Google Research elaborating on the NeurIPS 2024 paper on when and where transformers can outperform GNNs on graph tasks. A massive 3-part study of pooling in GNNs by Filippo Maria Bianchi (Arctic University of Norway) introduces the common pooling framework (select-reduce-connect), studies a variety of pooling methods and their evaluation protocols.