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.)