| CARVIEW |
Kijung Shin (신기정)
Associate ProfessorData Mining Lab, KAIST AI & EE
About Me
I am an Associate Professor in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division) at KAIST.
I received my Ph.D. in Computer Science from Carnegie Mellon University, where I was advised by Prof. Christos Faloutsos and supported by the KFAS Scholarship and the Siebel Scholar Fellowship.
I received my B.S. in Computer Science and Engineering and B.A. in Economics from Seoul National University. My research interests include data mining, graph algorithms, and network science.
At KAIST, I lead the Data Mining Lab.
Contact Details
Email: kijungs (at) kaist.ac.kr
Web: https://kijungs.github.io
Address:
Kim Jaechul Graduate School of AI, KAIST
85, Hoegi-ro, Dongdaemun-gu
Seoul, 02455, Republic of Korea
Education
Carnegie Mellon University
Ph.D. in Computer Science
•Feb. 2019
M.S. in Computer Science
•Dec. 2017
Seoul National University
B.S. in Computer Science and Engineering•Aug. 2015
B.A. in Economics (Double Major)
•Aug. 2015
Positions
KAIST
Associate Professor • Mar. 2023 - Present
Ewon Endowed Assistant Professor • Feb. 2019 - Feb. 2023
Research Intern • May. 2017 - Aug. 2017 & May. 2018 - Aug. 2018
CYRAM
Associate Researcher • Jan. 2011 - Dec. 2013
Teaching
KAIST EE210 Probability and Introductory Random Processes
Instructor • [ 2020 ]
KAIST EE209(B) Programming Structure for Electrical Engineering
Instructor • [ 2019 ]
CMU 10-601 Introduction to Machine Learning
Teaching Assistant • [ 2017 ]
CMU 15-780 Graduate Artificial Intelligence
Teaching Assistant • [ 2017 ]
Tutorials
[T2]
[T1]
Publications
[ Google Scholar | DBLP | Research Gate ]
2026 and Forthcoming
[C104]
Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption
Sunwoo Kim, Soo Yong Lee, Kyungho Kim, Hyunjin Hwang, Jaemin Yoo, and Kijung Shin
AAAI 2026
[ paper | slides | poster | code and datsets | bib ]
[C103]
2025
[C102]
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
NeurIPS 2025 (Benchmark Paper)
[ paper | poster | code and datasets | bib ]
[C101]
Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding
Sunwoo Kim, Hyunjin Hwang, and Kijung Shin
NeurIPS 2025
[ paper | poster | code and datasets | bib ]
[C100]
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
NeurIPS 2025
[ paper | bib ]
[C99]
Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms
Fanchen Bu, Ruochen Yang, Paul Bogdan, and Kijung Shin
ICDM 2025
[ paper | video | slides | code and datasets | bib ]
Selected as one of the best-ranked papers of ICDM 2025 for fast-track journal invitation
[C98]
HyperSearch: Prediction of New Hyperedges through Unconstrained yet Efficient Search
Hyunjin Choo, Fanchen Bu, Hyunjin Hwang, Young-Gyu Yoon, and Kijung Shin
ICDM 2025
[ paper | video | slides | code and datasets | bib ]
[C97]
Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes
Jaewan Chun*, Seokbum Yoon*, Minyoung Choe, Geon Lee, and Kijung Shin
ICDM 2025
[ paper | video | slides | code and datasets | bib ]
Received the IEEE ICDM Best Paper Award [link]
Selected as one of the best-ranked papers of ICDM 2025 for fast-track journal invitation
[C96]
Identifying Group Anchors in Real-World Group Interactions Under Label Scarcity
Fanchen Bu, Geon Lee, Minyoung Choe, and Kijung Shin
ICDM 2025
[ paper | video | slides | code and datasets | bib ]
[C95]
'Hello, World!': Making GNNs Talk with LLMs
Sunwoo Kim, Soo Yong, Jaemin Yoo, and Kijung Shin
Findings of EMNLP 2025 (Short Paper)
[ paper | video | slides | poster | code and datasets | bib ]
[C94]
A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation
Kyungho Kim, Sunwoo Kim, Geon Lee, and Kijung Shin
CIKM 2025
[ paper | slides | code and datasets | bib ]
[C93]
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
KDD 2025 (Industry Paper)
[ paper | slides | poster | code and datasets | bib ]
[C92]
RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Federico Berto*, Chuanbo Hua*, Junyoung Park*, Laurin Luttmann*, Yining Ma, Fanchen Bu, Jiarui Wang,
Haoran Ye, Minsu Kim, Sanghyeok Choi, Zepeda Gast, Andre Hottung, Jianan Zhou, Jieyi Bi, Yu Hu, Fei Liu,
Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Davide Angioni, Wouter Kool, Zhiguang Cao, Jie Zhang, Kijung Shin,
Cathy Wu, Sungsoo Ahn, Guojie Song, Changhyun Kwon, Lin Xie, and Jinkyoo Park
KDD 2025 (Benchmark Paper)
[ paper | code and datasets | bib ]
[C91]
[C90]
Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
Langzhang Liang, Fanchen Bu, Zixing Song, Zenglin Xu, Shirui Pan, and Kijung Shin
ICML 2025
[ paper | code and datasets | bib ]
[C89]
KGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking
Juyeon Kim, Geon Lee, Taeuk Kim, and Kijung Shin
SIGIR 2025 (Short Paper)
[ paper | video | poster | code and datasets | bib ]
[C88]
MARIOH: Multiplicity-Aware Hypergraph Reconstruction
Kyuhan Lee, Geon Lee, and Kijung Shin
ICDE 2025
[ paper | appendix | code and datasets | bib ]
[C87]
[C86]
Multi-Behavior Recommender Systems: A Survey
Kyungho Kim, Sunwoo Kim, Geon Lee, Jinhong Jung, and Kijung Shin
PAKDD 2025 (Survey Paper)
[ paper | bib ]
Received the PAKDD Best Survey Paper Award [link]
[C85]
TiGer: Self-Supervised Purification for Time-evolving Graphs
Hyeonsoo Jo, Jongha Lee, Fanchen Bu and Kijung Shin
PAKDD 2025
[ paper | code and datasets | bib ]
[C84]
[C83]
Beyond Neighbors: Distance-Generalized Graphlets for Enhanced Graph Characterization
Yeongho Kim, Yuyeong Kim, Geon Lee, and Kijung Shin
WWW 2025
[ paper | poster | code and datasets | bib ]
[C82]
DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation
Junghun Lee, Hyunju Kim, Fanchen Bu, Jihoon Ko, and Kijung Shin
AAAI 2025
[ paper | poster | code and datasets | bib ]
[C81]
[C80]
RASP: Robust Mining of Frequent Temporal Sequential Patterns under Temporal Variations
Hyunjin Choo, Minho Eom, Gyuri Kim, Young-Gyu Yoon, and Kijung Shin
EDBT 2025
[ paper | slides | code and datasets | bib ]
[J33]
Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation
Geon Lee, Kyungho Kim, Fanchen Bu, Langzhang Liang, and Kijung Shin
ACM TORS
[ paper | shorter ver. [C76] | code and datasets | bib ]
[J32]
Inductive Influence Estimation and Maximization over Unseen Social Networks under Two Diffusion Models
Jihoon Ko*, Seojeong Kim*, Kyuhan Lee, Shinhwan Kang, Dongyeong Hwang, Kijung Shin, and Noseong Park
Data Mining and Knowledge Discovery
[ paper | shorter ver. [C28] | code and datasets | bib ]
[J31]
Gene Expression Inference Based on Graph Neural Networks Using L1000 Data
Tae Hyun Kim*, Harim Kim*, Hyunjin Hwang*, Shinwhan Kang*, Kijung Shin, and Inwha Baek
Briefings in Bioinformatics
[ paper | code and datasets | bib ]
[J30]
Effective and Lightweight Lossy Compression of Tensors: Techniques and Applications
Jihoon Ko, Taehyung Kwon, Jinhong Jung, and Kijung Shin
Knowledge and Information Systems
[ paper | shorter ver. [C78] | slides | code and datasets | bib ]
[J29]
[J28]
Estimating Simplet Counts via Sampling
Hyunju Kim*, Heechan Moon*, Fanchen Bu, Jihoon Ko, and Kijung Shin
The VLDB Journal
[ paper | shorter ver. [C52] | code and datasets | bib ]
[J27]
BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning
Jihoon Ko*, Shinhwan Kang*, Taehyung Kwon, Heechan Moon, and Kijung Shin
ACM TIST
[ paper | code and datasets | bib ]
[J26]
Compact Lossy Compression of Tensors via Neural Tensor-Train Decomposition
Taehyung Kwon, Jihoon Ko, Jinhong Jung, Jun-Gi Jang, and Kijung Shin
Knowledge and Information Systems
[ paper | shorter ver. [C60] | slides | code and datasets | bib ]
[W4]
2024
[C79]
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy
Sunwoo Kim, Soo Yong Lee, Fanchen Bu, Shinhwan Kang, Kyungho Kim, Jaemin Yoo, and Kijung Shin
NeurIPS 2024
[ paper | slides | poster | video | code and datasets | bib ]
[C78]
[C77]
[C76]
Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation
Geon Lee, Kyungho Kim, and Kijung Shin
RecSys 2024 (Short Paper)
[ paper | appendix | longer ver. [J33] | poster | code and datasets | bib ]
Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7)
[C75]
Towards Better Utilization of Multiple Views for Bundle Recommendation
Kyungho Kim, Sunwoo Kim, Geon Lee, and Kijung Shin
CIKM 2024 (Short Paper)
[ paper | appendix | poster | code and datasets | bib ]
[C74]
Post-Training Embedding Enhancement for Long-Tail Recommendation
Geon Lee, Kyungho Kim, and Kijung Shin
CIKM 2024 (Short Paper)
[ paper | appendix | poster | code and datasets | bib ]
[C73]
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
Sunwoo Kim*, Soo Yong Lee*, Yue Gao, Alessia Antelmi, Mirko Polato, and Kijung Shin
KDD 2024 (Survey Paper)
[ paper | slides | bib ]
[C72]
[C71]
[C70]
[C69]
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
Fanchen Bu, Hyeonsoo Jo, Soo Yong Lee, Sungsoo Ahn, and Kijung Shin
ICML 2024
[ paper | poster | code and datasets | bib ]
[C68]
[C67]
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Langzhang Liang, Sunwoo Kim, Kijung Shin, Zenglin Xu, Shirui Pan, and Yuan Qi
ICML 2024
[ paper | code and datasets | bib ]
[C66]
[C65]
VilLain: Self-Supervised Learning on Homogeneous Hypergraphs without Features via Virtual Label Propagation
Geon Lee, Soo Yong Lee, and Kijung Shin
WWW 2024
[ paper | poster | code and datasets | bib ]
[C64]
Self-Guided Robust Graph Structure Refinement
Yeonjun In, Kanghoon Yoon, Kibum Kim, Kijung Shin, and Chanyoung Park
WWW 2024
[ paper | code and datasets | bib ]
[C63]
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Sunwoo Kim, Shinhwan Kang, Fanchen Bu, Soo Yong Lee, Jaemin Yoo, and Kijung Shin
ICLR 2024
[ paper | poster | code and datasets | bib ]
[C62]
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs
Dongjin Lee, Juho Lee, and Kijung Shin
AAAI 2024
[ paper | poster | code and datasets | bib ]
[C61]
[J25]
Representative and Back-In-Time Sampling from Real-world Hypergraphs
Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
ACM TKDD
[ paper | shorter ver. [C40] | code and datasets | bib ]
[J24]
Deep Learning Model for Heavy Rainfall Nowcasting in South Korea
Seok-Geun Oh, Seok-Woo Son, Young-Ha Kim, Chanil Park, Jihoon Ko, Kijung Shin, Ji-Hoon Ha, and Hyesook Lee
Weather and Climate Extremes
[ paper | bib ]
[J23]
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
[ paper | slides | poster | code and datasets | bib ]
[J22]
Hypergraph Motifs and Their Extensions Beyond Binary
Geon Lee*, Seokbum Yoon*, Jihoon Ko, Hyunju Kim, and Kijung Shin
The VLDB Journal
[ paper | shorter ver. [C26] | code and datasets | bib ]
[W3]
Prediction Is NOT Classification: On Formulation and Evaluation of Hyperedge Prediction
Taehyung Yu, Soo Yong Lee, Hyunjin Hwang, and Kijung Shin
HDM 2024
[ paper | code and datasets | bib ]
[W2]
Graphlets over Time: A New Lens for Temporal Network Analysis
Deukryeol Yoon, Dongjin Lee, Minyoung Choe, and Kijung Shin
MLH 2024
[ paper | slides | code and datasets | bib ]
2023
[C60]
TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions
Taehyung Kwon, Jihoon Ko, Jinhong Jung, and Kijung Shin
ICDM 2023
[ paper | longer ver. [J26] | slides | code and datasets | bib ]
Received the IEEE ICDM Best Student Paper Runner-up Award [link]
Selected as one of the best-ranked papers of ICDM 2023 for fast-track journal invitation
[C59]
Robust Graph Clustering via Meta Weighting for Noisy Graphs
Hyeonsoo Jo, Fanchen Bu, and Kijung Shin
CIKM 2023
[ paper | slides | code and datasets | bib ]
[C58]
You’re Not Alone in Battle: Combat Threat Analysis Using Attention Networks and a New Open Benchmark
Soo Yong Lee*, Juwon Kim*, Kiwoong Park, Dongkuk Ryu, Sangheun Shim, and Kijung Shin
CIKM 2023 (Short Paper)
[ paper | poster | code and datasets | bib ]
[C57]
[C56]
On Improving the Cohesiveness of Graphs by Merging Nodes: Formulation, Analysis, and Algorithms
Fanchen Bu and Kijung Shin
KDD 2023
[ paper | appendix | longer ver. | video | slides | poster | code and datasets | bib ]
[C55]
[C54]
[C53]
[C52]
Characterization of Simplicial Complexes Using Simplets Beyond Four Nodes
Hyunju Kim, Jihoon Ko, Fanchen Bu, and Kijung Shin
WWW 2023
[ paper | appendix | longer ver. [J28] | video | slides | code and datasets | bib ]
[C51]
[C50]
[C49]
Robust and Efficient Alignment of Calcium Imaging Data through Simultaneous Low Rank and Sparse Decomposition
Junmo Cho*, Seungjae Han*, Eun-Seo Cho, Kijung Shin, and Young-Gyu Yoon
WACV 2023 [ paper | code and dataset | bib ]
[J21]
Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators
Sunwoo Kim, Minyoung Choe, Jaemin Yoo, and Kijung Shin
Data Mining and Knowledge Discovery
[ paper | shorter ver. [C48] | slides | code and datasets | bib ]
[J20]
Datasets, Tasks, and Training Methods for Large-Scale Hypergraph Learning
Sunwoo Kim*, Dongjin Lee*, Yul Kim, Jungho Park, Taeho Hwang, and Kijung Shin
Data Mining and Knowledge Discovery
[ paper | slides | poster | code and datasets | bib ]
[J19]
Improving the Core Resilience of Real-world Hypergraphs
Manh Tuan Do and Kijung Shin
Data Mining and Knowledge Discovery
[ paper | slides | poster | code and datasets | bib ]
[J18]
Hypercore Decomposition for Non-Fragile Hyperedges: Concepts, Algorithms, Observations, and Applications
Fanchen Bu, Geon Lee, and Kijung Shin
Data Mining and Knowledge Discovery
[ paper | slides | poster | code and datasets | bib ]
[J17]
Interplay between Topology and Edge Weights in Real-World Graphs: Concepts, Patterns, and an Algorithm
Fanchen Bu, Shinhwan Kang, and Kijung Shin
Data Mining and Knowledge Discovery
[ paper | slides | poster | code and datasets | bib ]
[J16]
Temporal Hypergraph Motifs
Geon Lee and Kijung Shin
Knowledge and Information Systems
[ paper | shorter ver. [C35] | code and datasets | bib ]
[J15]
Evaluation of Deep-Learning-Based Very Short-Term Rainfall Forecasts in South Korea
Seok-Geun Oh , Chanil Park, Seok-Woo Son, Jihoon Ko, Kijung Shin, Sunyoung Kim, and Junsang Park
Asia-Pacific Journal of Atmospheric Sciences
[ paper | bib ]
[J14]
Two-Stage Training of Graph Neural Networks for Graph Classification
Manh Tuan Do, Noseng Park, and Kijung Shin
Neural Processing Letters
[ paper | code and datasets | bib ]
A shorter version appeared at DLG-AAAI 2021
2022
[C48]
Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators
Sunwoo Kim, Minyoung Choe, Jaemin Yoo, and Kijung Shin
ICDM 2022
[ paper | longer ver. [J21] | slides | code and datasets | bib ]
[C47]
Set2Box: Similarity Preserving Representation Learning for Sets
Geon Lee, Chanyoung Park, and Kijung Shin
ICDM 2022
[ paper | longer ver. | slides | code and datasets | bib ]
[C46]
[C45]
HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams
Geon Lee, Minyoung Choe, and Kijung Shin
IJCAI 2022
[ paper | appendix | video | slides | code and datasets | bib ]
[C44]
AHP: Learning to Negative Sample for Hyperedge Prediction
Hyunjin Hwang*, Seungwoo Lee*, Chanyoung Park, and Kijung Shin
SIGIR 2022 (Short Paper)
[ paper | video | slides | code and datasets | bib ]
[C43]
Are Edge Weights in Summary Graphs Useful? - A Comparative Study
Shinhwan Kang, Kyuhan Lee, and Kijung Shin
PAKDD 2022
[ paper | appendix | slides | code and datasets | bib ]
[C42]
[C41]
[C40]
MiDaS: Representative Sampling from Real-world Hypergraphs
Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
WWW 2022
[ paper | appendix | longer ver. [J25] | video | slides | code and datasets | bib ]
[C39]
[C38]
[C37]
[C36]
Directed Network Embedding with Virtual Negative Edges
Hyunsik Yoo*, Yeon-Chang Lee*, Kijung Shin, and Sang-Wook Kim
WSDM 2022
[ paper | slides | code and datasets | bib ]
[J13]
Growth Patterns and Models of Real-world Hypergraphs
Jihoon Ko*, Yunbum Kook*, and Kijung Shin
Knowledge and Information Systems
[ paper | shorter ver. [C27] | code and datasets | bib ]
[J12]
Effective Training Strategies for Deep-Learning-Based Precipitation Nowcasting and Estimation
Jihoon Ko*, Kyuhan Lee*, Hyunjin Hwang*, Seok-Geun Oh, Seok-Woo Son, and Kijung Shin
Computers and Geosciences
[ paper | code | bib ]
[J11]
Simple Epidemic Models with Segmentation Can Be Better than Complex Ones
Geon Lee, Se-eun Yoon, and Kijung Shin
PLOS ONE
[ paper | appendix | slides | code and datasets | bib ]
A shorter version appeared at epiDAMIK-KDD 2021 (long oral)
[J10]
Real-Time Anomaly Detection in Edge Streams
Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos
ACM TKDD
[ paper | shorter ver. [C20] | code and datasets | bib ]
[W1]
Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data
Jihoon Ko*, Kyuhan Lee*, Hyunjin Hwang, and Kijung Shin
SSTDM 2022
[ paper | slides | code and datasets | bib ]
2021
[C35]
THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting
Geon Lee and Kijung Shin
ICDM 2021
[ paper | appendix | longer ver. [J16] | video | slides | code and datasets | bib ]
Selected as one of the best-ranked papers of ICDM 2021 for fast-track journal invitation
[C34]
Efficient Neural Network Approximation of Robust PCA for Automated Analysis of
Calcium Imaging Data
Seungjae Han, Eun-Seo Cho, Inkyu Park, Kijung Shin, and Young-Gyu Yoon
MICCAI 2021
[ paper | code and datasets | bib ]
[C33]
[C32]
[C31]
[C30]
[C29]
DPGS: Degree-Preserving Graph Summarization
Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, Huawei Shen, and Xueqi Cheng
SDM 2021
[ paper | code and datasets | bib ]
[J9]
CoCoS: Fast and Accurate Distributed Triangle Counting in Graph Streams
Kijung Shin, Euiwoong Lee, Jinoh Oh, Mohammad Hammoud, and Christos Faloutsos
ACM TKDD
[ paper | shorter ver. [C13] | code and datasets | bib ]
2020
[C28]
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks
Jihoon Ko, Kyuhan Lee, Kijung Shin, and Noseong Park
ASONAM 2020
[ paper | longer ver. [J32] | slides | code and datasets | bib ]
Selected for fast-track journal invitation
[C27]
Evolution of Real-world Hypergraphs: Patterns and Models without Oracles
Yunbum Kook, Jihoon Ko, and Kijung Shin
ICDM 2020
[ paper | longer ver. [J13] | video | slides | code and datasets | bib ]
Selected as one of the best-ranked papers of ICDM 2020 for fast-track journal invitation
[C26]
Hypergraph Motifs: Concepts, Algorithms, and Discoveries
Geon Lee, Jihoon Ko, and Kijung Shin
VLDB 2020
[ paper | appendix | longer ver. [J22] | video |
slides | code and datasets | bib ]
[C25]
Incremental Lossless Graph Summarization
Jihoon Ko*, Yunbum Kook*, and Kijung Shin
KDD 2020
[ paper | video (short) | video (long) | slides | code and datasets | bib ]
[C24]
SSumM: Sparse Summarization of Massive Graphs
Kyuhan Lee*, Hyeonsoo Jo*, Jihoon Ko, Sungsu Lim, and Kijung Shin
KDD 2020
[ paper | video (short) | video (long) | slides | code and datasets | bib ]
[C23]
Structural Patterns and Generative Models of Real-world Hypergraphs
Manh Tuan Do, Se-eun Yoon, Bryan Hooi, and Kijung Shin
KDD 2020
[ paper | appendix | video (short) | video (long) | slides | code and datasets | bib ]
[C22]
[C21]
[C20]
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos
AAAI 2020
[ paper | longer ver. [J10] | code and datasets | bib ]
[J8]
Temporal Locality-Aware Sampling for Accurate Triangle Counting in Real Graph Streams
Dongjin Lee, Kijung Shin, and Christos Faloutsos
The VLDB Journal
[ paper | shorter ver. [C12] | code and datasets | bib ]
[J7]
Fast and Memory-Efficient Algorithms for High-Order Tucker Decomposition
Jiyuan Zhang, Jinoh Oh, Kijung Shin, Evangelos E. Papalexakis, Christos Faloutsos, and Hwanjo Yu
Knowledge and Information Systems
[ paper | shorter ver. [C7] | code | bib ]
[J6]
Fast, Accurate and Provable Triangle Counting in Fully Dynamic Graph Streams
Kijung Shin, Sejoon Oh, Jisu Kim, Bryan Hooi, and Christos Faloutsos
ACM TKDD
[ paper | shorter ver. [C16] | code and datasets | bib ]
2019
[C19]
[C18]
[C17]
[D3]
Mining Large Dynamic Graphs and Tensors
Kijung Shin
Ph.D. Thesis, Carnegie Mellon University, 2019
[ paper | slides | code and datasets | bib ]
2018
[C16]
Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions
Kijung Shin, Jisu Kim, Bryan Hooi, and Christos Faloutsos
PKDD 2018
[ paper | appendix | longer ver. [J6] | slides | code and datasets | bib ]
[C15]
[C14]
Discovering Progression Stages in Trillion-Scale Behavior Logs
Kijung Shin, Mahdi Shafiei, Myunghwan Kim, Aastha Jain, and Hema Raghavan
WWW 2018 (Industry Track)
[ paper | slides | bib ]
[C13]
Tri-Fly: Distributed Estimation of Global and Local Triangle Counts in Graph Streams
Kijung Shin, Mohammad Hammoud, Euiwoong Lee, Jinoh Oh, and Christos Faloutsos
PAKDD 2018
[ paper | appendix | longer ver. [J9] | slides | code and datasets | bib ]
[J5]
Fast, Accurate and Flexible Algorithms for Dense Subtensor Mining
Kijung Shin, Bryan Hooi, and Christos Faloutsos
ACM TKDD
[ paper | shorter ver. [C5] | code and datasets | bib ]
[J4]
Patterns and Anomalies in k-Cores of Real-World Graphs with Applications
Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos
Knowledge and Information Systems
[ paper | shorter ver. [C6] | code and datasets | bib ]
Taught in courses: MIT (6.886)
[D2]
Mining Large Dynamic Graphs and Tensors: Thesis Proposal
Kijung Shin
Ph.D. Thesis Proposal, Carnegie Mellon University, 2018
[ paper | slides | code and datasets ]
2017
[C12]
WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams
Kijung Shin
ICDM 2017
[ paper | appendix |
longer ver. [J8] |
slides | code and datasets | bib ]
[C11]
ZooRank: Ranking Suspicious Entities in Time-Evolving Tensors
Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, and Jürgen Pfeffer
PKDD 2017
[ paper | code and datasets | bib ]
[C10]
[C9]
Why You Should Charge Your Friends for Borrowing Your Stuff
Kijung Shin, Euiwoong Lee, Dhivya Eswaran, and Ariel D. Procaccia
IJCAI 2017
[ paper |
slides (short) | slides (long) | bib ]
Media: New Scientist [link]
[C8]
D-Cube: Dense-Block Detection in Terabyte-Scale Tensors
Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos
WSDM 2017
[ paper | appendix | longer ver. | slides | code and datasets | bib ]
Selected for long oral presentation
[C7]
S-HOT: Scalable High-Order Tucker Decomposition
Jinoh Oh, Kijung Shin, Evangelos E. Papalexakis, Christos Faloutsos, and Hwanjo Yu
WSDM 2017
[ paper | longer ver. [J7] | code | bib ]
[J3]
Graph-Based Fraud Detection in the Face of Camouflage
Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, and Christos Faloutsos
ACM TKDD
[ paper | shorter ver. [C4] | code | bib ]
[J2]
Fully Scalable Methods for Distributed Tensor Factorization
Kijung Shin, Lee Sael, and U Kang
IEEE TKDE
[ paper | appendix | shorter ver. [C2] | code and datasets | bib ]
2016
[C6]
CoreScope: Graph Mining Using k-Core Analysis - Patterns, Anomalies and Algorithms
Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos
ICDM 2016
[ paper | appendix | longer ver. [J4] | slides | code and datasets | bib ]
Selected as one of the best-ranked papers of ICDM 2016 for fast-track journal invitation
[C5]
M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees
Kijung Shin, Bryan Hooi, and Christos Faloutsos
PKDD 2016
[ paper | appendix | longer ver. [5] | slides | code and datasets | bib ]
[C4]
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos
KDD 2016
[ paper | longer ver. [J3] | code | bib ]
Received the SIGKDD Best Research Paper Award [link] and the CogX Award for Best Student Paper in AI [link]
Media: NSF [link], WESA [link], TechXplore [link], Stanford Scholar [link], Crain's [link]
[J1]
Random Walk with Restart on Large Graphs Using Block Elimination
Jinhong Jung, Kijung Shin, Lee Sael, and U Kang
ACM TODS
[ paper | shorter ver. [C3] | code and datasets | bib ]
2015
[D1]
[C3]
BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs
Kijung Shin, Jinhong Jung, Lee Sael, and U Kang
SIGMOD 2015
[ paper | longer ver. [J1] | slides | code and datasets | bib ]
Received the Samsung Humantech Paper Award (1st in Computer Science) [link],
Taught in courses: UMich (EECS 598)
2014
[C2]
Distributed Methods for High-dimensional and Large-scale Tensor Factorization
Kijung Shin and U Kang
ICDM 2014
[ paper | longer ver. [J2] | slides | code and datasets | bib ]
Software
[ GitHub ]NetMiner 4 - Social Network Analysis Software
NetMiner is an application software for exploratory analysis and visualization of large network data based on SNA (Social Network Analysis). This tool allows researchers to explore their network data visually and interactively, helps them to detect underlying patterns and structures of the network.
[ web | wiki | free trial ] • Participation: Jan. 2011 - Dec. 2013
Professional Service
Conference Program Chair (or Track Chair)
[C2]
The Web Conference (WWW)
"Graph Algorithms and Modeling for the Web" Track Co-Chair • 2026
[C1]
ACM International Conference on Information and Knowledge Management (CIKM)
Short-Paper Program Co-Chair • 2025
Conference Senior Program Committee (or Area Chair)
[S3]
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
Senior Program Committee (Area Chair) • 2023 - 2026
[S2]
Conference on Neural Information Processing Systems (NeurIPS)
Area Chair (Datasets and Benchmark Track) • 2023 - 2025
[S1]
International Conference on Learning Representations (ICLR)
Area Chair • 2025
Conference Program Committee (or Reviewer)
[P8]
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
Program Committee • 2019 - 2022
[P7]
The Web Conference (WWW)
Program Committee • 2019 - 2025
[P6]
International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Program Committee • 2025
[P5]
IEEE International Conference on Data Mining (ICDM)
Program Committee • 2019 - 2025
[P4]
ACM Conference on Web Search and Data Mining (WSDM)
Program Committee • 2022 - 2026
[P3]
SIAM International Conference on Data Mining (SDM)
Program Committee • 2022 - 2025
[P2]
ACM International Conference on Information and Knowledge Management (CIKM)
Program Committee • 2021 - 2024
[P1]
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
Program Committee • 2023 - 2024, 2026
Conference Organizing Committee
[O4]
IEEE International Conference on Data Mining (ICDM)
Tutorial Co-Chair • 2026
[O3]
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
Hands-On Tutorial Co-Chair • 2026
[O2]
International Conference on Database Systems for Advanced Applications (DASFAA)
Tutorial Co-Chair • 2026
[O1]
IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Publicity Co-Chair • 2024
Workshop Organizing Committee
[W1]
Workshop on Graph Learning with Foundation Models
Workshop Co-chair • 2025
[W2]
Workshop on Mining and Learning Real-world Dynamics via High-order Networks
Workshop Co-chair • 2024
Journal Editors
[J1]
Big Data Research
Associate Editor • Aug. 2022 - July. 2025