activity
20232026
most citedSpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces

3 citations · 4 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2026

Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs

Jaejun Lee, Joyce Jiyoung Whang

While Virtual Nodes (VNs) are often utilized in Message Passing Neural Networks (MPNNs) to facilitate effective message passing, existing VN-based methods have limitations, such as…

cs.LG2026★ 1 cited

Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion

Jaejun Lee, Seheon Kim, Joyce Jiyoung Whang

Hyper-relational knowledge graphs (HKGs) effectively represent complex facts. While inferring new knowledge in HKGs is a critical problem, current methods cast it as a simple link…

cs.AI2025

AssurAI: Experience with Constructing Korean Socio-cultural Datasets to Discover Potential Risks of Generative AI

Chae-Gyun Lim, Seung-Ho Han, EunYoung Byun +51

The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric, failing to capture specific risks i…

cs.IR2025

Unifying Inductive, Cross-Domain, and Multimodal Learning for Robust and Generalizable Recommendation

Chanyoung Chung, Kyeongryul Lee, Sunbin Park +1

Recommender systems have long been built upon the modeling of interactions between users and items, while recent studies have sought to broaden this paradigm by generalizing to new…

cs.AI2025

SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sector

Kyeongryul Lee, Heehyeon Kim, Joyce Jiyoung Whang

The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes,…

cs.LG2024

Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors

Jinhyeok Choi, Heehyeon Kim, Joyce Jiyoung Whang

Graph neural networks (GNNs) have emerged as an effective tool for fraud detection, identifying fraudulent users, and uncovering malicious behaviors. However, attacks against GNN-b…

cs.LG2024★ 3 cited

SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces

Jinhyeok Choi, Heehyeon Kim, Minhyeong An +1

Spatio-temporal graph (STG) forecasting is a critical task with extensive applications in the real world, including traffic and weather forecasting. Although several recent methods…

cs.LG2023

Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection

Heehyeon Kim, Jinhyeok Choi, Joyce Jiyoung Whang

Fraud detection aims to discover fraudsters deceiving other users by, for example, leaving fake reviews or making abnormal transactions. Graph-based fraud detection methods conside…