246 citations · 269 across the 7 of their papers we have counts for
7 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…
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…
Predicting Information Pathways Across Online Communities
Yiqiao Jin, Yeon-Chang Lee, Kartik Sharma +4
The problem of community-level information pathway prediction (CLIPP) aims at predicting the transmission trajectory of content across online communities. A successful solution to…
A Survey of Graph Neural Networks for Social Recommender Systems
Kartik Sharma, Yeon-Chang Lee, Sivagami Nambi +4
Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions as well as the user-to-user social relations for the task of generating item recommendat…
Representation Learning in Continuous-Time Dynamic Signed Networks
Kartik Sharma, Mohit Raghavendra, Yeon Chang Lee +2
Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such…
Linear, or Non-Linear, That is the Question!
Taeyong Kong, Taeri Kim, Jinsung Jeon +4
There were fierce debates on whether the non-linear embedding propagation of GCNs is appropriate to GCN-based recommender systems. It was recently found that the linear embedding p…