Researcher · KAIST AI

Jaewan Chun 전재완

Ph.D. student in Artificial Intelligence

I'm a Ph.D. student in the Data Mining Lab at KAIST AI, advised by Professor Kijung Shin. My research focuses on data mining, especially hypergraph mining and spatio-temporal prediction.

I received my M.S. in Artificial Intelligence from KAIST, and my B.S. in Computer Science and Electrical Engineering (double major), also from KAIST.

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Jaewan Chun

Research interests

Publications

[J3] A Survey on Centrality and Importance Measures in Hypergraphs: Categorization and Empirical Insights.

Jaewan Chun*, Fanchen Bu*, Yeongho Kim, Atsushi Miauchi, Francesco Bonchi, and Kijung Shin

ACM Computing Surveys · 2026

  • Hypergraph mining
  • Centrality
Paper Code

[P1] Automating and Scaling Behavioral Scientific Research on AI Agents.

Soo Yong Lee*, Jongha Lee*, Jaewan Chun, Hyunjin Hwang, Fanchen Bu, Ziv Ben-Zion, Taekwan Kim, Denny Borsboom, Jaemin Yoo, and Kijung Shin

arXiv 2608.10030 · 2026

  • AI agents
Paper

[J2] From binary to general attributes: attributed hypergraph generation with realistic interplay between structure and attributes.

Jaewan Chun, Seokbum Yoon, Minyoung Choe, Geon Lee, and Kijung Shin

Knowledge and Information Systems · 2026

Invited as one of the best-ranked papers of ICDM 2025

  • Hypergraph mining
  • Graph generation
Paper Code Slides Shorter version [C1]

[C1] Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes.

Jaewan Chun*, Seokbum Yoon*, Minyoung Choe, Geon Lee, and Kijung Shin

IEEE International Conference on Data Mining (ICDM) · 2025 (Full paper acceptance rate = 13.5%)

IEEE ICDM Best Paper Award

Selected as one of the best-ranked papers of ICDM 2025 for fast-track journal invitation [J2]

Invited to the Sister Conferences’ Best Paper Track of IJCAI 2026 with an extended abstract

  • Hypergraph mining
  • Graph generation
Paper Code Slides Poster Extended Abstract (IJCAI ’26)

[J1] Random Walk with Restart on Hypergraphs: Fast Computation and an Application to Anomaly Detection.

Jaewan Chun, Geon Lee, Kijung Shin†, and Jinhong Jung†

Data Mining and Knowledge Discovery · 2023 (ECML PKDD 2024 Journal Track)

  • Hypergraph mining
  • Anomaly detection
Paper Code Slides Poster