most citedCAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship

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cs.LG2024★ 2 cited

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…

cs.LG2024

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…