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Education

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)

Publications & Lectures

Tutorial

• [T1] S. Kim*, S. Lee*, Y. Gao, A. Antelmi, M. Polato, and K. Shin. Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. In KDD 2024 (Lecture-style tutorial)

Preprints

• [P1] H. Moon, H. Kim, S. Kim, and K. Shin. Four-set Hypergraphlets for Characterization of Directed Hypergraphs. 2023

2024

• [C10] S. Kim*, S. Lee*, Y. Gao, A. Antelmi, M. Polato, and K. Shin. A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. In KDD 2024 (survey paper)

• [C9] J. Lee, S. Kim, and K. Shin. SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning. In KDD 2024

• [C8] S. Lee, S. Kim, F. Bu, J. Yoo, J. Tang, and K. Shin. Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective. In ICML 2024

• [C7] L. Liang, S. Kim, K. Shin, Z. Xu, S. Pan, and Y. Qi. Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs. In ICML 2024

• [J3] Y. Shin, S. Kim, and W. Shin. Prototype-Based Explanations for Graph Neural Networks (Extended version of [C1]). In TPAMI (Transactions on Pattern Analysis and Machine Intelligence) 2024

• [C6] D. Hwang, H. Kim, S. Kim, and K. Shin. FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer. In CVPR 2024 

• [C5] S. Kim, S. Kang, F. Bu, S. Lee, J. Yoo, and K. Shin. HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs. In ICLR 2024 

2023

• [J2] S. Kim*, D. Lee*, Y. Kim, J. Park, T. Hwang, and K. Shin. Datasets, tasks, and training methods for large-scale hypergraph learning. In DAMI (Data Mining and Knowledge Discovery) 2023

• [J1] S. Kim, M. Choe, J. Yoo, and K. Shin. Reciprocity in Directed Hypergraphs: Measures, Patterns, and Generators (Extended version of [C2]). In DAMI (Data Mining and Knowledge Discovery) 2023

• [C4] M. Choe, S. Kim, J. Yoo, and K. Shin. Classification of Edge-dependent Labels of Nodes in Hypergraphs. In KDD 2023

• [C3] S. Kim, F. Bu, M. Choe, J. Yoo, and K. Shin. How Transitive Are Real-World Group Interactions? - Measurement and Reproduction. In KDD 2023

2022

• [C2] S. Kim, M. Choe, J. Yoo, and K. Shin. Reciprocity in Directed Hypergraphs: Measures, Patterns, and Generators. In ICDM 2022

• [C1] Y. Shin, S. Kim, E. Yoon, and W. Shin. Prototype-Based Explanations for Graph Neural Networks (Student Abstract). In AAAI 2022 (Student Abstract Track)

Experience

Academic Reviewer

• NeurIPS 2024

• KDD 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

Project 

• 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