Post #663

Fresh Picks from ArXiv The past week on GraphML arXiv: scaling up GNNs, heterophily, expressivity, sparse equivariant graph networks, and applications ranging from particle physics to electronic health records. Scaling up GNNs: - Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations ft. Open Catalyst Project team. - PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication Heterophily: - Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily - Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily Theory: - GraphCoCo: Graph Complementary Contrastive Learning - Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport - SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks ft. Christopher Morris. - Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification - Exploring High-Order Structure for Robust Graph Structure Learning Surveys: - Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges ft. Savannah Thais. - Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey - A systematic approach to random data augmentation on graph neural networks Applications: - 3D Human Pose Estimation Using Möbius Graph Convolutional Networks ft. Emanuele Rodola. - Graph-Text Multi-Modal Pre-trainingfor Medical Representation Learning - Sequence-to-Sequence Knowledge Graph Completion and Question Answering - Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis - Ethereum Fraud Detection with Heterogeneous Graph Neural Networks - Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings ft. Shuiwang Ji. (If I forgot to mention your paper, please shoot me a message and I will update the post.)