Post #813

GraphML News (Dec 17th) - The NeurIPS edition, TGB and TpuGraphs NeurIPS’23 happened this week in New Orleans with 3000+ papers, 50+ workshops and competitions, and 16000+ registered participants. The most important part of such enormous events is networking, and, based on my impressions, the Graph ML community is thriving with so many new ideas and projects (especially after attending the workshops). We will be reflecting on the hot trends, ideas that fell out of favor / are solved, and update the predictions in the annual 2023-2024 post which is already in the works (so stay tuned). PS > All the flow matching t-shirts found their owners 😉 New blogposts: - Temporal Graph Benchmark by Andy Huang and Emanuele Rossi - introduces TGB, its design principles and supported tasks - Advancements in ML for ML by Google on the new TpuGraphs dataset, Graph Segment Training for large graphs, and the recently finished Kaggle competition on the TpuGraphs dataset (GraphSAGE is in the most of the top winning solutions) Weekend reading: A Hitchhiker’s Guide to Geometric GNNs for 3D Atomic Systems by Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, et al - a handbook on geometric GNNs, see our previous post for more details Are Graph Neural Networks Optimal Approximation Algorithms? by Morris Yau feat. Stefanie Jegelka - introduces OptGNN that performs very competitively on a bunch of combinatorial optimization tasks TorchCFM, the main library for conditional flow matching, released a bunch of new tutorials in Jupyter notebooks - winter holidays are a perfect time to learn more about flow matching and optimal transport