📃 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!)