My name is Sunwoo Kim, and I am a Ph.D. student in the Data Mining Lab (advisor: Prof. Kijung Shin) at KAIST AI.
From March to June 2026, I’ll be joining Snap Research (User Modeling & Personalization Team) as a research intern.
My research interests include
Adapting foundation models to recommendations
Self-supervised representation learning
Representation learning on (hyper)graphs
Contact:
Ph.D.
Duration: 03. 2024. ~ ING
School: Korea Advanced Institute of Science and Technology (KAIST)
Dept.: Graduate School of Kim Jaechul AI
Advisor: Prof. Kijung Shin
Master
Duration: 03. 2022. ~ 02. 2024.
School: Korea Advanced Institute of Science and Technology (KAIST)
Dept.: Graduate School of Kim Jaechul AI
Advisor: Prof. Kijung Shin
Thesis: How Transitive Are Real-World Group Interactions? - Measurement and Reproduction [KDD 2023]
Bachelor
Duration: 03. 2016. ~ 02. 2022.
School: Yonsei University (Seoul)
Major: Applied Statistics (GPA: 4.1/4.3)
Tutorial
[T1] Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide.
S. Kim*, S. Lee*, Y. Gao, A. Antelmi, M. Polato, and K. Shin.
In CIKM 2025 (to be presented)
In AAAI 2025
In KDD 2024
In ICDM 2024
2026
[C18] Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption
S. Kim, S. Lee, H. Hwang, K. Kim, J. Yoo, and K. Shin
In AAAI 2026
2025
[C17] Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding.
S. Kim, H. Hwang, and K. Shin
In NeurIPS 2025
[C16] RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases.
D. Choi, S. Kim, J. Kim, K. Kim, G. Lee, S. Kang, M. Kim, and K. Shin
In NeurIPS 2025 (Dataset & Benchmark)
[C15] 'Hello, World!': Making GNNs Talk with LLMs.
S. Kim, S. Lee, J. Yoo, and K. Shin
In EMNLP Findings 2025 (short)
[C14] A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation.
K. Kim, S. Kim, G. Lee, and K. Shin
In CIKM 2025
[C13] Multi-Behavior Recommender Systems: A Survey.
K. Kim, S. Kim, G. Lee, J. Jung, and K. Shin
In PAKDD 2025 (survey)
2024
[C12] Rethinking Reconstruction-based Graph-level Anomaly Detectors: Limitations and a Simple Remedy.
S. Kim, S. Lee, F. Bu, S. Kang, K. Kim, J. Yoo, and K. Shin
In NeurIPS 2024
[C11] Towards Better Utilization of Multiple Views for Bundle Recommendation.
K. Kim, S. Kim, G. Lee, and K. Shin
In CIKM 2024 (short)
[C10] A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide.
S. Kim*, S. Lee*, Y. Gao, A. Antelmi, M. Polato, and K. Shin
In KDD 2024 (survey)
[C9] SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning.
J. Lee, S. Kim, and K. Shin
In KDD 2024
[C8] Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective.
S. Lee, S. Kim, F. Bu, J. Yoo, J. Tang, and K. Shin.
In ICML 2024
[C7] Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs.
L. Liang, S. Kim, K. Shin, Z. Xu, S. Pan, and Y. Qi
In ICML 2024
[J3] Prototype-Based Explanations for Graph Neural Networks (Extended version of [C1]).
Y. Shin, S. Kim, and W. Shin
In TPAMI (Transactions on Pattern Analysis and Machine Intelligence) 2024
[C6] FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer.
D. Hwang, H. Kim, S. Kim, and K. Shin
In CVPR 2024
[C5] HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs.
S. Kim, S. Kang, F. Bu, S. Lee, J. Yoo, and K. Shin.
In ICLR 2024
2023
[J2] Datasets, tasks, and training methods for large-scale hypergraph learning.
S. Kim*, D. Lee*, Y. Kim, J. Park, T. Hwang, and K. Shin
In DAMI (Data Mining and Knowledge Discovery) 2023
In ECML-PKDD 2023 (journal track paper)
[J1] Reciprocity in Directed Hypergraphs: Measures, Patterns, and Generators (Extended version of [C2]).
S. Kim, M. Choe, J. Yoo, and K. Shin
In DAMI (Data Mining and Knowledge Discovery) 2023
[C4] Classification of Edge-dependent Labels of Nodes in Hypergraphs.
M. Choe, S. Kim, J. Yoo, and K. Shin.
In KDD 2023
[C3] How Transitive Are Real-World Group Interactions? - Measurement and Reproduction.
S. Kim, F. Bu, M. Choe, J. Yoo, and K. Shin
In KDD 2023
2022
[C2] Reciprocity in Directed Hypergraphs: Measures, Patterns, and Generators.
S. Kim, M. Choe, J. Yoo, and K. Shin.
In ICDM 2022
[C1] Prototype-Based Explanations for Graph Neural Networks.
Y. Shin, S. Kim, E. Yoon, and W. Shin.
In AAAI 2022 (student abstract)
Preprints
[P1] Four-set Hypergraphlets for Characterization of Directed Hypergraphs.
H. Moon, H. Kim, S. Kim, and K. Shin.
Award
Outstanding survey paper award, PAKDD, 2025 (C13).
Outstanding reviewer, KDD, 2025.
Outstanding paper award, KAIST, 2024 (J1).
Best Undergraduate Student Paper Award, KICS, 2021.
Academic Reviewer
Conference
NeurIPS 2024, 2025
KDD 2024, 2025, 2026
ICLR 2025, 2026
ICML 2025
AAAI 2026
CIKM 2025
AISTATS 2025, 2026
WWW 2026
Journal
Artificial intelligence 2024
Military Service
Duration: 09. 2017. ~ 05. 2019
Division: 2nd (currently 12th) Infantry Division of the Republic Of Korea Army
Role: Information soldier
Rank: Sergeant
Projects
Mutable neural network
Duration: 01. 2024 ~ ING
Affiliation: ETRI
User-device matching and Household-device matching
Duration: 04. 2022. ~ 12. 2023.
Affiliation: Samsung Research
Researcher
Student Researcher
Duration: 01. 2021. ~ 11. 2021.
School: Yonsei University (Seoul)
Dept.: Computational Engineering Science (MIDAS Lab)
Advisor: Prof. Won Yong Shin