Post #666

Fresh Picks from Arxiv The past week on GraphML arXiv: Hypergraph NNs, GNNs are dynamic programmers, latent graph learning, 3D equivariant molecule generation, and a new GNN library for Keras. △ Hypergraph Neural Networks: - Message Passing Neural Networks for Hypergraphs - Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs ft. Yu Rong. - Preventing Over-Smoothing for Hypergraph Neural Networks ⅀ Theory: - Graph Neural Networks are Dynamic Programmers ft. Petar Veličković. - OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks - Shift-Robust Node Classification via Graph Adversarial Clustering ft. Jiawei Han. - Mutual information estimation for graph convolutional neural networks - Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications ft. Michael Bronstein. 🏐Equivariance and 3D Graphs: - Equivariant Diffusion for Molecule Generation in 3D ft. Max Welling. - 3D Equivariant Graph Implicit Functions 📚Libraries and Surveys: - GNNkeras: A Keras-based library for Graph Neural Networks and homogeneous and heterogeneous graph processing ft. Franco Scarselli. - Graph Neural Networks in IoT: A Survey 🔨Applications: - Graph similarity learning for change-point detection in dynamic networks ft. Xiowen Dong. - Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment ft. Yizhou Sun. - A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning - Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design ft. Pieter Abbeel. (If I forgot to mention your paper, please shoot me a message and I will update the post.)