3 citations · 9 across the 10 of their papers we have counts for
8 papers · 1 filter
Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction
Van Thuy Hoang, O-Joun Lee
Molecular property prediction is becoming one of the major applications of graph learning in Web-based services, e.g., online protein structure prediction and drug discovery. A key…
Pre-training Graph Neural Networks on 2D and 3D Molecular Structures by using Multi-View Conditional Information Bottleneck
Van Thuy Hoang, O-Joun Lee
Recent pre-training strategies for molecular graphs have attempted to use 2D and 3D molecular views as both inputs and self-supervised signals, primarily aligning graph-level repre…
Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products
Van Thuy Hoang, Tien-Bach-Thanh Do, Jinho Seo +5
The growing demand for halal cosmetic products has exposed significant challenges, especially in Muslim-majority countries. Recently, various machine learning-based strategies, e.g…
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…
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…
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
Van Thuy Hoang, O-Joun Lee
Graph representation learning (GRL) methods, such as graph neural networks and graph transformer models, have been successfully used to analyze graph-structured data, mainly focusi…
Connector 0.5: A unified framework for graph representation learning
Thanh Sang Nguyen, Jooho Lee, Van Thuy Hoang +1
Graph representation learning models aim to represent the graph structure and its features into low-dimensional vectors in a latent space, which can benefit various downstream task…