Our team member, Van Thuy Hoang, presented his paper, which is titled “Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity” and depicts his novel graph transformer model called “Unified Graph Transformer,” at the 38th AAAI Conference on Artificial Intelligence (AAAI 2024). This recognition demonstrated his dedication and expertise in graph learning.
- Van Thuy Hoang, O-Joun Lee: Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity. The 38th AAAI Conference on Artificial Intelligence (AAAI 2024), Vancouver, Canada; 02/2024.
We also presented at the Explainable Machine Learning for Sciences Workshop (XAI4Sci) in conjunction with the 38th AAAI Conference on Artificial Intelligence (AAAI 2024). This presentation demonstrated our commitment and expertise in applying deep learning methods to scientific research.
- Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon, O-Joun Lee: Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation. The Explainable Machine Learning for Sciences Workshop (XAI4Sci) held in conjunction with the 38th AAAI Conference on Artificial Intelligence (AAAI 2024), Vancouver, Canada; 02/2024.
