Graph Machine Learning
Graph ML News (May 27th): New Antibiotic found with Geometric DL, Differential Privacy, NeurIPS Submissions A new antibiotic abaucin is discovered by the power of Geometric Deep Learning! Abaucin targets a stubborn Acinetobacter baumannii pathogen resistant to many drugs. The new Nature Chem Bio paper (feat. Regina Barzilay and Tommi Jaakkola from MIT) sheds more light on the screening process and used methods. Stanford launches the online version of the flagship CS224W course of Graph ML. The 10-credit course is priced at $1,750 and starts on June 5th. The TAG in ML workshop on topology announced a new challenge: implementing more topology-enabled neural nets with the TopoModelX framework where top contributors will become co-authors of a JMLR submission. That’s a great option for those who’d like to start working with topological neural architectures! Vincent Cohen-Addad and Alessandro Epasto of Google Research published a post on differentiably-private clustering: introducing an approach for DP hierarchical clustering with formal guarantees and lower bounds, and an approach for large-scale DP clustering. The Weekend Reading section this week is brought to you by NeurIPS submissions, quite a number of cool papers: Link Prediction for Flow-Driven Spatial Networks - the work introduces the Graph Attentive Vectors (GAV) framework for link prediction (based on the labeling trick commonly used in LP) and smashes the OGB-Vessel leaderboard with a 10-points rocauc margin to the previous SOTA. Edge Directionality Improves Learning on Heterophilic Graphs feat. Emanuele Rossi, Francesco Di Giovanni, Fabrizio Frasca, Michael Bronstein, and Stephan Günnemann PRODIGY: Enabling In-context Learning Over Graphs feat. Qian Huang, Hongyu Ren, Percy Liang, and Jure Leskovec - a cool attempt to bring prompting to the permutation-invariant nature of graphs. Uncertainty Quantification over Graph with Conformalized Graph Neural Networks feat. Kexin Huang and Jure Leskovec — one of the first works on Conformal Prediction with GNNs. Learning Large Graph Property Prediction via Graph Segment Training feat. Jure Leskovec and Bryan Perozzi ChatDrug - a neat attempt at combining ChatGPT with retrieval plugins and molecular models to edit molecules, peptides, and proteins right with natural language. Extension of MoleculeSTM that we featured in the recent State of Affairs post. MISATO - Machine learning dataset for structure-based drug discovery - a new dataset of 20K protein-ligand complexes with molecular dynamics traces and electronic properties. Multi-State RNA Design with Geometric Multi-Graph Neural Networks feat. Chaitanya Joshi and Pietro Lio
