4 papers
CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship
Yeon-Chang Lee, JaeHyun Lee, Michiharu Yamashita +2
The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit th…
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks
Yeon-Chang Lee, Hojung Shin, Sang-Wook Kim
Graph Neural Networks (GNNs) have become essential tools for graph representation learning in various domains, such as social media and healthcare. However, they often suffer from…
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation
Neng Kai Nigel Neo, Yeon-Chang Lee, Yiqiao Jin +2
The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive…
Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
Min-Jeong Kim, Yeon-Chang Lee, David Y. Kang +1
The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. Whil…