3 citations · 10 across the 8 of their papers we have counts for
8 papers
Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
Van Thuy Hoang, O-Joun Lee
This study aims to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to o…
Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation
Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon +1
This paper investigates the impact of observations on atmospheric state estimation in weather forecasting systems using graph neural networks (GNNs) and explainability methods. We…
CloudNine: Analyzing Meteorological Observation Impact on Weather Prediction Using Explainable Graph Neural Networks
Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon +1
The impact of meteorological observations on weather forecasting varies with sensor type, location, time, and other environmental factors. Thus, quantitative analysis of observatio…
A Survey on Structure-Preserving Graph Transformers
Van Thuy Hoang, O-Joun Lee
The transformer architecture has shown remarkable success in various domains, such as natural language processing and computer vision. When it comes to graph learning, transformers…
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
Van Thuy Hoang, O-Joun Lee
This study utilizes community structures to address node degree biases in message-passing (MP) via learnable graph augmentations and novel graph transformers. Recent augmentation-b…
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning
Van Thuy Hoang, Sang Thanh Nguyen, Sangmyeong Lee +3
Knowledge graph (KG) embedding has been used to benefit the diagnosis of animal diseases by analyzing electronic medical records (EMRs), such as notes and veterinary records. Howev…