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Geon Lee @ KAIST
Geon Lee
Ph.D. Student, KAIST
geonlee0325 (at) kaist.ac.kr
I am a Ph.D. student in the Kim Jaechul Graduate School of AI at KAIST, advised by Prof. Kijung Shin at the Data Mining Lab. I received my B.S. in Computer Science and Engineering from Sungkyunkwan University. I have also interned at Snap Research, NEC Labs America and Amazon. My research interests include graph mining & learning, recommender systems, retrieval systems, time series analysis, multi-modal learning, and social network analysis.
🎓 Education
KAIST
Seoul, South Korea
M.S. & Ph.D. in Artificial Intelligence
Sep. 2020 - Present
Sungkyunkwan University
Suwon, South Korea
B.S. in Computer Science and Engineering
Mar. 2016 - Aug. 2019
💼 Work Experience
Snap Research
Bellevue, WA, USA
Research Intern
April 2025 - June 2025
NEC Labs America
Princeton, NJ, USA
Research Intern
May 2023 - Aug. 2023
Amazon
San Francisco, CA, USA
Applied Scientist Intern
Sep. 2022 - Dec. 2022
📚 Publications
2026
[C25] Sequential Data Augmentation for Generative Recommendation
Geon Lee, Bhuvesh Kumar, Clark Mingxuan Ju, Tong Zhao, Kijung Shin, Neil Shah, and Liam Collins
2025
[C24] RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases
Dongwon Choi, Sunwoo Kim, Juyeon Kim, Kyungho Kim, Geon Lee, Shinhwan Kang, Myunghwan Kim, and Kijung Shin
[C23] TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
Yushan Jiang, Wenchao Yu, Geon Lee, Dongjin Song, Kijung Shin, Wei Cheng, Yanchi Liu, and Haifeng Chen
[C22] Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes
Jaewan Chun, Seokbum Yoon, Minyoung Choe, Geon Lee, and Kijung Shin
Best Paper Award
[C21] Identifying Group Anchors in Real-World Group Interactions Under Label Scarcity
Fanchen Bu, Geon Lee, Minyoung Choe, and Kijung Shin
[C20] A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation
Kyungho Kim, Sunwoo Kim, Geon Lee, and Kijung Shin
[J8] Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation
Geon Lee, Kyungho Kim, Fanchen Bu, Langzhang Liang, and Kijung Shin
[C19] SkySearch: Satellite Video Search at Scale
Minyoung Choe*,
Geon Lee*,
Changhun Han*,
Suji Kim, Woong Hu, Hyebeen Hwang, Geunseok Park, Byeongyeon Kim, Hyesook Lee, Ha-Myung Park, and Kijung Shin
[C18] KGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking
Juyeon Kim, Geon Lee, Taeuk Kim, and Kijung Shin
[C17] MARIOH: Multiplicity-Aware Hypergraph Reconstruction
Kyuhan Lee, Geon Lee, and Kijung Shin
[J7] A Survey on Hypergraph Mining: Patterns, Tools, and Generators
Geon Lee*, Fanchen Bu*, Tina Eliassi-Rad, and Kijung Shin
[C16] Multi-Behavior Recommender Systems: A Survey
Kyungho Kim, Sunwoo Kim, Geon Lee, Jinhong Jung, and Kijung Shin
Best Survey Paper Award
[C15] Beyond Neighbors: Distance-Generalized Graphlets for Enhanced Graph Characterization
Yeongho Kim, Yuyeong Kim, Geon Lee, and Kijung Shin
[C14] TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
Geon Lee, Wenchao Yu, Kijung Shin, Wei Cheng, and Haifeng Chen
2024
[C13] Resource2Box: Learning to Rank Resources in Distributed Search Using Box Embedding
Ulugbek Ergashev, Geon Lee, Kijung Shin, Eduard Dragut, and Weiyi Meng
[C12] Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation
Geon Lee, Kyungho Kim, and Kijung Shin
One of the Best Short Paper Candidates
[C11] Post-Training Embedding Enhancement for Long-Tail Recommendation
Geon Lee, Kyungho Kim, and Kijung Shin
[C10] Towards Better Utilization of Multiple Views for Bundle Recommendation
Kyungho Kim, Sunwoo Kim, Geon Lee, and Kijung Shin
[J6] Representative and Back-in-Time Sampling from Real-World Hypergraphs
Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
[C9] VilLain: Self-Supervised Learning on Homogeneous Hypergraphs without Features via Virtual Label Propagation
Geon Lee, Soo Yong Lee, and Kijung Shin
2023
[T2] Mining of Real-World Hypergraphs: Patterns, Tools, and Generators
Geon Lee, Jaemin Yoo, and Kijung Shin
[J5] Random Walk with Restart on Hypergraphs: Fast Computation and an Application to Anomaly Detection
Jaewan Chun, Geon Lee, Kijung Shin, and Jinhong Jung
[J4] Hypergraph Motifs and Their Extensions Beyond Binary
Geon Lee*, Seokbum Yoon*, Jihoon Ko, Hyunju Kim, and Kijung Shin
KAIST Outstanding Paper Award
[J3] Hypercore Decomposition for Non-Fragile Hyperedges: Concepts, Algorithms, Observations, and Applications
Fanchen Bu, Geon Lee, and Kijung Shin
[J2] Temporal Hypergraph Motifs
Geon Lee and Kijung Shin
2022
[T1] Mining of Real-World Hypergraphs: Patterns, Tools, and Generators
Geon Lee, Jaemin Yoo, and Kijung Shin
[C8] Set2Box: Similarity Preserving Representation Learning for Sets
Geon Lee, Chanyoung Park, and Kijung Shin
[C7] HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams
Geon Lee, Minyoung Choe, and Kijung Shin
[C6] MiDaS: Representative Sampling from Real-World Hypergraphs
Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
[J1/W1] Simple Epidemic Models with Segmentation Can Be Better than Complex Ones
Geon Lee, Se-eun Yoon, and Kijung Shin
2021
[C5] THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting
Geon Lee and Kijung Shin
One of the Best-Ranked Papers
[C4] How Do Hyperedges Overlap in Real-World Hypergraphs? - Patterns, Measures, and Generators
Geon Lee*, Minyoung Choe*, and Kijung Shin
2020
[C3] Hypergraph Motifs: Concepts, Algorithms, and Discoveries
Geon Lee, Jihoon Ko, and Kijung Shin
[C2] MEGA: Multi-View Semi-Supervised Clustering of Hypergraphs
Joyce Jiyoung Whang, Rundong Du, Sangwon Jung, Geon Lee, Barry Drake, Qingqing Liu, Seonggoo Kang, and Haesun Park
2019
[C1] Hyperlink Classification via Structured Graph Embedding
Geon Lee, Seonggoo Kang, and Joyce Jiyoung Whang
📑 Academic Services
Program Committee/Conference Reviewer
KDD (2023 - 2026) |
AAAI (2024 - 2026) |
WWW (2024 - 2026) |
ICLR (2025 - 2026) |
AISTATS (2025 - 2026) |
CIKM (2022 - 2025) |
LoG (2022 - 2025) |
NeurIPS (2024 - 2025) |
RecSyS (2025) |
ICML (2025) |
ACML (2025) |
SDM (2024)
Journal Reviewer
TNNLS (2023 - 2025) |
The VLDB Journal (2023 - 2025) |
Information Sciences (2024 - 2025) |
PLOS ONE (2024 - 2025) |
Data Mining and Knowledge Discovery (2024 - 2025) |
Scientific Reports (2025) |
Neural Networks (2025) |
TOIS (2025) |
TPAMI (2025) |
Information Processing and Management (2025) |
The Journal of Supercomputing (2025) |
Machine Learning (2025) |
Patterns (2025) |
TKDE (2023 - 2024) |
TNSE (2024) |
Big Data Research (2024)
Session Chair
CIKM (2024)
🏆 Awards & Honors
- Received the Best Paper Award at ICDM 2025 Dec. 2025
- Received the Best Survey Paper Award at PAKDD 2025 June 2025
- Selected as the Outstanding Reviewer of KDD 2025 Dec. 2024
- Received KAIST Outstanding Paper Award Nov. 2024
- Selected as One of the Best Short Paper Candidates at RecSys 2024 Oct. 2024
- Selected as One of the Best-Ranked Papers of ICDM 2021 Dec. 2021
- Received Sungkyunkwan Presidential Award Aug. 2019
- Received Sungkyunkwan Software Scholarship (Full tuition for all semesters) 2016 - 2019
🚀 Projects
- StarLab (EntireDB2AI) Jan. 2025 -
- AI-Based Weather Forecast Support Development July 2021 - Dec. 2024
- COVID-19 Task Force Mar. 2020 - Sep. 2020
📘 Teaching
Teaching Assistant
- KAIST AI506 Data Mining and Search Spring 2021, Spring 2023
- KAIST AI607 Graph Mining and Social Network Analysis Fall 2020, Fall 2021, Fall 2022, Fall 2023
- KAIST AI617 Machine Learning for Robotics Spring 2022
- SKKU CSE3036 Seminar in Computer Engineering Fall 2019
Tutorial Tutor
- Mining of Real-World Hypergraphs: Patterns, Tools, and Generators CIKM 2022, ICDM 2022, WWW 2023, KDD 2023