Post #761

GraphML News, March 25th edition Some news you might have missed in the graph learning area after the week of massive AGI claims and GPT plugins announcement. ICLR 2023 announced Outstanding Papers - great to see two GNN papers there! One Outstanding Award went to Rethinking the Expressive Power of GNNs via Graph Biconnectivity, an honorable mention went to Conditional Antibody Design as 3D Equivariant Graph Translation. New releases of the main graph libraries: - PyG announced 2.3.0 with the full PyTorch 2.0 support where scatter and sparse APIs are now parts of the main torch, so you might expect less hassle installing PyG dependencies now. Besides, new torch.compile() brings 2-3x speed improvements for many common GNN architectures. - DGL presented a new version 1.0 at the recent LoGaG reading group, the video recording is already available. The new version introduces a new sparse API and further scalability improvements. New papers for the weekend reading: A Survey on Oversmoothing in Graph Neural Networks by T. Konstantin Rusch, Michael Bronstein, and Siddhartha Mishra - everything you wanted to know about known sources of oversmoothing and ways to alleviate it - including the recent Gradient Gating framework we reviewed a while ago. Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design by Kexin Huang, Payal Chandak, et al - introduces TxGNN, a pre-trained GNN for identifying therapeutic opportunities for diseases with limited treatment options (and completely new diseases in the zero-shot manner).