activity
20242026
most citedDSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning

10 citations · 13 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification

Junghoon Kim, Seungyoon Choi, Hyunsung Kim +1

Entities in real-world systems often evolve over time: users shift between information consumption patterns, researchers migrate between fields, and firms transition between financ…

cs.LG2026

DefaultGNN: A Dual-Perspective GNN Framework for Predicting Corporate Default from Buyer-Seller Transaction Networks

Junghoon Kim, Hyunsung Kim, Seungyoon Choi +4

Corporate default prediction is a core problem in financial risk management, yet traditional credit models rely heavily on financial statements that are often sparse or unavailable…

cs.LG2026

Compositional Spectral Prompts for LLM-based Online Time Series Forecasting

Seungyoon Choi, Hyunchul Kim, Jae-Gil Lee +1

To address the sequential and evolving nature of time series, the Online Time Series Forecasting (OTSF) task has been extensively studied in multiple domains. Existing research foc…

cs.LG2025

Dynamic Time-aware Continual User Representation Learning

Seungyoon Choi, Sein Kim, Hongseok Kang +2

Traditional user modeling (UM) approaches have primarily focused on designing models for a single specific task, but they face limitations in generalization and adaptability across…

cs.IR2024★ 3 cited

Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System

Sein Kim, Hongseok Kang, Seungyoon Choi +3

Collaborative filtering recommender systems (CF-RecSys) have shown successive results in enhancing the user experience on social media and e-commerce platforms. However, as CF-RecS…

cs.LG2024★ 10 cited

DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning

Seungyoon Choi, Wonjoong Kim, Sungwon Kim +3

We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods. Existing rehearsal-based GCL methods select the most representative nodes…