Prototype-Based Explanations for Graph Neural Networks (Student Abstract). - Yong-Min Shin; Sun-Woo Kim 006; Eun-Bi Yoon; Won-Yong Shin (AAAI, 2022)

Classification of Edge-dependent Labels of Nodes in Hypergraphs. - Minyoung Choe; Sunwoo Kim 006; Jaemin Yoo; Kijung Shin (KDD, 2023)

How Transitive Are Real-World Group Interactions? - Measurement and Reproduction. - Sunwoo Kim 006; Fanchen Bu; Minyoung Choe; Jaemin Yoo; Kijung Shin (KDD, 2023)

Four-set Hypergraphlets for Characterization of Directed Hypergraphs. - Heechan Moon; Hyunju Kim; Sunwoo Kim 006; Kijung Shin (CoRR, 2023)

Reciprocity in directed hypergraphs: measures, findings, and generators. - Sunwoo Kim 006; Minyoung Choe; Jaemin Yoo; Kijung Shin (Data Min. Knowl. Discov., 2023)

Datasets, tasks, and training methods for large-scale hypergraph learning. - Sunwoo Kim 006; Dongjin Lee; Yul Kim; Jungho Park; Taeho Hwang; Kijung Shin (Data Min. Knowl. Discov., 2023)

Towards Better Utilization of Multiple Views for Bundle Recommendation. - Kyungho Kim; Sunwoo Kim 006; Geon Lee; Kijung Shin (CIKM, 2024)

FlowerFormer: Empowering Neural Architecture Encoding Using a Flow-Aware Graph Transformer. - Dongyeong Hwang; Hyunju Kim; Sunwoo Kim 006; Kijung Shin (CVPR, 2024)

HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs. - Sunwoo Kim 006; Shinhwan Kang; Fanchen Bu; Soo Yong Lee; Jaemin Yoo; Kijung Shin (ICLR, 2024)

Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective. - Soo Yong Lee; Sunwoo Kim 006; Fanchen Bu; Jaemin Yoo; Jiliang Tang; Kijung Shin (ICML, 2024)

Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs. - Langzhang Liang; Sunwoo Kim 006; Kijung Shin; Zenglin Xu; Shirui Pan; Yuan Qi 001 (ICML, 2024)

A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. - Sunwoo Kim 006; Soo Yong Lee; Yue Gao 002; Alessia Antelmi; Mirko Polato; Kijung Shin (KDD, 2024)

SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning. - Jongha Lee; Sunwoo Kim 006; Kijung Shin (KDD, 2024)

Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy. - Sunwoo Kim 006; Soo Yong Lee; Fanchen Bu; Shinhwan Kang; Kyungho Kim; Jaemin Yoo; Kijung Shin (NeurIPS, 2024)

PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks. - Yong-Min Shin; Sun-Woo Kim 006; Won-Yong Shin (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Multi-behavior Recommender Systems: A Survey. - Kyungho Kim; Sunwoo Kim 006; Geon Lee; Jinhong Jung; Kijung Shin (PAKDD (4), 2025)