Post #661

📃 Fresh Picks from ArXiv The past week on the GraphML ArXiv digest: A flurry of new survey papers, GNNs for molecular property prediction and NLP/KG, as well as new avenues in GNN modelling. 📚 Surveys: - Generative models for molecular discovery: Recentadvances and challenges. ft. Wengong Jin, Tommi Jaakkola, Regina Barzilay. - Explainability in Graph Neural Networks: An Experimental Survey. - A Survey on Deep Graph Generation: Methods and Applications. - Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review. - Few-Shot Learning on Graphs: A Survey. 🧬 GNNs for Science: - Protein Representation Learning by Geometric Structure Pretraining. ft. Jian Tang. - Multimodal Learning on Graphs for Disease Relation Extraction. ft. Marinka Zitnik. - MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties. - Simulating Liquids with Graph Networks. 🗣 GNNs for NLP and Knowledge Graphs: - A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs. ft. Mikhail Galkin. - Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models. - AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension. - HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations. 🌐 GNN Modelling and Applications: - GRAND+: Scalable Graph Random Neural Networks. ft. Jie Tang. - Graph Representation Learning with Individualization and Refinement. ft. Lee Wee Sun. - Graph Augmentation Learning. - SoK: Differential Privacy on Graph-Structured Data. - Incorporating Heterophily into Graph Neural Networks for Graph Classification. - Supervised Contrastive Learning with Structure Inference for Graph Classification. (If I forgot to mention your paper, please shoot me a message and I will update the post. We will be trying to resume the 'Fresh Picks from ArXiv' series every Monday morning!)