When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for
capturing complex dependencies within diverse graph-structured data. Despite their …
capturing complex dependencies within diverse graph-structured data. Despite their …
GOAT: Learning Multi-Body Dynamics Using Graph Neural Network with Restrains
S Yang, D Jia, L Chen, K Li, F Wu… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Accurately simulating physical processes is an extremely challenging task, but the rapid
development of machine learning and the availability of large datasets have made Graph …
development of machine learning and the availability of large datasets have made Graph …
Graph Causal Contrastive for Partial Label Learning
J Yuan, F Wu, J Zhao - International Conference on Intelligent Computing, 2024 - Springer
Graph representation learning is gaining attention due to topological graphs' versatility in
modeling real-world data. While graph neural networks are powerful for this, they often …
modeling real-world data. While graph neural networks are powerful for this, they often …