4 papers
THeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation
Ming-Yi Hong, Miao-Chen Chiang, Youchen Teng +3
Heterogeneous Graph Neural Networks (HGNNs) are effective for modeling Heterogeneous Information Networks (HINs), which encode complex multi-typed entities and relations. However,…
Style4Rec: Enhancing Transformer-based E-commerce Recommendation Systems with Style and Shopping Cart Information
Berke Ugurlu, Ming-Yi Hong, Che Lin
Understanding users' product preferences is essential to the efficacy of a recommendation system. Precision marketing leverages users' historical data to discern these preferences…
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation
Ming-Yi Hong, Yi-Hsiang Huang, Shao-En Lin +3
Graph Neural Networks (GNNs) excel in delineating graph structures in diverse domains, including community analysis and recommendation systems. As the interpretation of GNNs become…
A GAN Approach for Node Embedding in Heterogeneous Graphs Using Subgraph Sampling
Hung-Chun Hsu, Bo-Jun Wu, Ming-Yi Hong +2
Graph neural networks (GNNs) face significant challenges with class imbalance, leading to biased inference results. To address this issue in heterogeneous graphs, we propose a nove…