[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: AAAI Conference on Artificial Intelligence 2026

[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): Annual Conference on Neural Information Processing Systems 2025

[C101] Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding

Sunwoo Kim, Hyunjin Hwang, and Kijung Shin

NeurIPS 2025: Annual Conference on Neural Information Processing Systems 2025

[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: Annual Conference on Neural Information Processing Systems 2025

[C99] Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms

Fanchen Bu, Ruochen Yang, Paul Bogdan, and Kijung Shin

ICDM 2025: IEEE International Conference on Data Mining 2025

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: IEEE International Conference on Data Mining 2025

[C97] Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes

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

ICDM 2025: IEEE International Conference on Data Mining 2025

Received the IEEE ICDM Best Paper Award

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: IEEE International Conference on Data Mining 2025

[C95] 'Hello, World!': Making GNNs Talk with LLMs

Sunwoo Kim, Soo Yong Lee, Jaemin Yoo, and Kijung Shin

EMNLP 2025 (Findings, Short Paper): Conference on Empirical Methods in Natural Language Processing 2025

[C94] A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation

Kyungho Kim, Sunwoo Kim, Geon Lee, and Kijung Shin

CIKM 2025: ACM Conference on Information and Knowledge Management 2025

[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): ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2025

[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): ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2025

[C91] On Measuring Unnoticeability of Graph Adversarial Attacks: Observations, New Measure, and Applications

Hyeonsoo Jo*, Hyunjin Hwang*, Fanchen Bu, Soo Yong Lee, Chanyoung Park,  and Kijung Shin

KDD 2025: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2025

[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: International Conference on Machine Learning 2025

[C89] KGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking

Juyeon Kim, Geon Lee, Taeuk Kim,  and Kijung Shin

SIGIR 2025 (Short Paper): International ACM SIGIR Conference on Research and Development in Information Retrieval 2025

[C88] MARIOH: Multiplicity-Aware Hypergraph Reconstruction

Kyuhan Lee, Geon Lee, and Kijung Shin

ICDE 2025: IEEE International Conference on Data Engineering 2025

[C87] Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts

Jongha Lee, Taehyung Kwon, Heechan Moon, and Kijung Shin

ICDE 2025: IEEE International Conference on Data Engineering 2025

[C86] Multi-Behavior Recommender Systems: A Survey

Kyungho Kim, Sunwoo Kim, Geon Lee, Jinhong Jung, and Kijung Shin

PAKDD 2025 (Survey Paper): Pacific-Asia Conference on Knowledge Discovery and Data Mining 2025

Received the PAKDD Best Survey Paper Award

[C85] TiGer: Self-Supervised Purification for Time-evolving Graphs

Hyeonsoo Jo, Jongha Lee, Fanchen Bu, and Kijung Shin

PAKDD 2025: Pacific-Asia Conference on Knowledge Discovery and Data Mining 2025

[C84] Kronecker Generative Models for Power-Law Patterns in Real-World Hypergraphs

Minyoung Choe, Jihoon Ko, Taehyung Kwon, Kijung Shin, and Christos Faloutsos

WWW 2025: The Web Conference 2025

[C83] Beyond Neighbors: Distance-Generalized Graphlets for Enhanced Graph Characterization

Yeongho Kim, Yuyeong Kim, Geon Lee,  and Kijung Shin

WWW 2025: The Web Conference 2025

[C82] DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation

Junghun Lee, Hyunju Kim, Fanchen Bu, Jihoon Ko,  and Kijung Shin

AAAI 2025: AAAI Conference on Artificial Intelligence 2025

[C81] 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

AAAI 2025: AAAI Conference on Artificial Intelligence 2025

[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: International Conference on Extending Database Technology 2025

[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: ACM Transactions on Recommender Systems

[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

[J31] Gene Expression Inference Based on Graph Neural Networks Using L1000 Data

Tae Hyun Kim*, Harim Kim*, Hyunjin Hwang*, Shinwhan Kang*, Kijung Shin, and Inhwa Baek

Briefings in Bioinformatics

[J30] Effective and Lightweight Lossy Compression of Tensors: Techniques and Applications

 Jihoon Ko, Taehyung Kwon, Jinhong Jung, and Kijung Shin

Knowledge and Information Systems

[J29] A Survey on Hypergraph Mining: Patterns, Tools, and Generators

Geon Lee*, Fanchen Bu, Tina Eliassi-Rad, and Kijung Shin

ACM Computing Surveys

[J28] Estimating Simplet Counts via Sampling

Hyunju Kim*, Heechan Moon*, Fanchen Bu, Jihoon Ko, and Kijung Shin

The VLDB Journal

[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: ACM Transactions on Intelligent Systems and Technology

[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

[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: Annual Conference on Neural Information Processing Systems 2024

[C78] ELiCiT: Effective and Lightweight Lossy Compression of Tensors

Jihoon Ko, Taehyung Kwon, Jinhong Jung, and Kijung Shin

ICDM 2024: IEEE International Conference on Data Mining 2024

Selected as one of the best-ranked papers of ICDM 2024 for fast-track journal invitation  

[C77] Resource2Box: Learning To Rank Resources in Distributed Search Using Box Embedding

Ulugbek Ergashev, Geon Lee, Kijung Shin, Eduard Dragut, and Weiyi Meng

ICDM 2024: IEEE International Conference on Data Mining 2024

[C76] Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation

Geon Lee, Kyungho Kim, and Kijung Shin

RecSys 2024 (Short Paper): ACM Conference on Recommender Systems 2024

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): ACM Conference on Information and Knowledge Management 2024

[C74] Post-Training Embedding Enhancement for Long-Tail Recommendation

Geon Lee, Kyungho Kim, and Kijung Shin

CIKM 2024 (Short Paper): ACM Conference on Information and Knowledge Management 2024

[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): ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2024

[C72] Compact Decomposition of Irregular Tensors for Data Compression: From Sparse to Dense to High-Order Tensors

Taehyung Kwon, Jihoon Ko, Jinhong Jung, Jun-Gi Jang, and Kijung Shin

KDD 2024: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2024

[C71] SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning

Jongha Lee, Sunwoo Kim, and Kijung Shin

KDD 2024: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2024

[C70] Unsupervised Alignment of Hypergraphs with Different Scales

Manh Tuan Do and Kijung Shin

KDD 2024: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2024

[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: International Conference on Machine Learning 2024

[C68] Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective

Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo, Jiliang Tang, and Kijung Shin

ICML 2024: International Conference on Machine Learning 2024

[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: International Conference on Machine Learning 2024

[C66] FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer

Dongyeong Hwang, Hyunju Kim, Sunwoo Kim, and Kijung Shin

CVPR 2024: IEEE/CVF Computer Vision and Pattern Recognition Conference 2024

[C65] VilLain: Self-Supervised Learning on Homogeneous Hypergraphs without Features via Virtual Label Propagation

Geon Lee, Soo Yong Lee, and Kijung Shin

WWW 2024: The Web Conference 2024

[C64] Self-Guided Robust Graph Structure Refinement

Yeonjun In, Kanghoon Yoon, Kibum Kim, Kijung Shin, and Chanyoung Park

WWW 2024: The Web Conference 2024

[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: International Conference on Learning Representations 2024

[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: AAAI Conference on Artificial Intelligence 2024

[C61] VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation

Taeri Kim, Jiho Heo, Hongil Kim, Kijung Shin, and Sang-Wook Kim

AAAI 2024: AAAI Conference on Artificial Intelligence 2024

Selected for oral presentation (2.6% of accepted papers) 

[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: ACM Transactions on Knowledge Discovery from Data

[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

[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

[J22] Hypergraph Motifs and Their Extensions Beyond Binary

Geon Lee*, Seokbum Yoon*, Jihoon Ko, Hyunju Kim, and Kijung Shin

The VLDB Journal

[W3] Prediction Is NOT Classification: On Formulation and Evaluation of Hyperedge Prediction

Taehyung Yu, Soo Yong Lee, Hyunjin Hwang, and Kijung Shin

HDM 2024: Workshop on High Dimensional Data Mining

[C60] TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions

Taehyung Kwon, Jihoon Ko, Jinhong Jung, and Kijung Shin

ICDM 2023:  IEEE International Conference on Data Mining 2023

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: ACM International Conference on Information and Knowledge Management 2023

[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): ACM International Conference on Information and Knowledge Management 2023

[C57] How Transitive Are Real-World Group Interactions? - Measurement and Reproduction

Sunwoo Kim, Fanchen Bu, Minyoung Choe, Jaemin Yoo, and Kijung Shin

KDD 2023: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2023

[C56] On Improving the Cohesiveness of Graphs by Merging Nodes: Formulation, Analysis, and Algorithm

Fanchen Bu and Kijung Shin

KDD 2023: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2023

[C55] Classification of Edge-dependent Labels of Nodes in Hypergraphs

Minyoung Choe, Sunwoo Kim, Jaemin Yoo, and Kijung Shin

KDD 2023: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2023

[C54] Towards Deep Attention in Graph Neural Networks: Problems and Remedies

Soo Yong Lee, Fanchen Bu, Jaemin Yoo, and Kijung Shin

ICML 2023: International Conference on Machine Learning 2023

[C53] NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors

Taehyung Kwon*, Jihoon Ko*, Jinhong Jung, and Kijung Shin

WWW 2023: The Web Conference 2023

[C52] Characterization of Simplicial Complexes Using Simplets Beyond Four Nodes

Hyunju Kim, Jihoon Ko, Fanchen Bu, and Kijung Shin

WWW 2023:  The Web Conference 2023

[C51] Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding

Hyunsik Yoo, Yeon-Chang Lee, Kijung Shin, and Sang-Wook Kim

WWW 2023: The Web Conference 2023

[C50] I'm Me, We're Us, and I'm Us: Tridirectional Contrastive Learning on Hypergraphs

Dongjin Lee and Kijung Shin

AAAI 2023: AAAI Conference on Artificial Intelligence 2023

[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: IEEE/CVF Winter Conference on Applications of Computer Vision 2023

[J21] Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators

Sunwoo Kim, Minyoung Choe, Jaemin Yoo, and Kijung Shin

Data Mining and Knowledge Discovery

[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

[J19] Improving the Core Resilience of Real-world Hypergraphs

Manh Tuan Do and Kijung Shin

Data Mining and Knowledge Discovery

[J18] Hypercore Decomposition for Non-Fragile Hyperedges: Concepts, Algorithms, Observations, and Applications

Fanchen Bu, Geon Lee, and Kijung Shin

Data Mining and Knowledge Discovery

[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

[J16] Temporal Hypergraph Motifs

Geon Lee and Kijung Shin

Knowledge and Information Systems

[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

[C48] Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators

Sunwoo Kim, Minyoung Choe, Jaemin Yoo, and Kijung Shin

ICDM 2022: IEEE International Conference on Data Mining 2022

[C47] Set2Box: Similarity Preserving Representation Learning for Sets

Geon Lee, Chanyoung Park, and Kijung Shin

ICDM 2022: IEEE International Conference on Data Mining 2022

[C46] MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation

Taeri Kim*, Yeon-Chang Lee*, Kijung Shin, and Sang-Wook Kim

CIKM 2022: ACM International Conference on Information and Knowledge Management 2022

[C45] HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams

Geon Lee, Minyoung Choe, and Kijung Shin

IJCAI 2022: International Joint Conference on Artificial Intelligence 2022

[C44] AHP: Learning to Negative Sample for Hyperedge Prediction

Hyunjin Hwang*, Seungwoo Lee*, Chanyoung Park, and Kijung Shin

SIGIR 2022 (Short Paper): International ACM SIGIR Conference on Research and Development in Information Retrieval 2022

[C43] Are Edge Weights in Summary Graphs Useful? - A Comparative Study

Shinhwan Kang, Kyuhan Lee, and Kijung Shin

PAKDD 2022:  Pacific-Asia Conference on Knowledge Discovery and Data Mining 2022

[C42] Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications

Shinhwan Kang, Kyuhan Lee, and Kijung Shin

ICDE 2022: IEEE International Conference on Data Engineering 2022

[C41] SLUGGER: Lossless Hierarchical Summarization of Massive Graphs

Kyuhan Lee*, Jihoon Ko*, and Kijung Shin

ICDE 2022: IEEE International Conference on Data Engineering 2022

[C40] MiDaS: Representative Sampling from Real-world Hypergraphs

Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin

WWW 2022: The Web Conference 2022

[C39] On the Persistence of Higher-Order Interactions in Real-World Hypergraphs

Hyunjin Choo and Kijung Shin

SDM 2022: SIAM International Conference on Data Mining 2022

[C38] Meta-Learning for Online Update of Recommender Systems

Minseok Kim, Hwanjun Song, Yooju Shin, Dongmin Park, Kijung Shin, and Jae-Gil Lee

AAAI 2022: AAAI Conference on Artificial Intelligence 2022

[C37] Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs

Hyeonjeong Shin, Taehyung Kwon, Neil Shah, and Kijung Shin

WSDM 2022: ACM International Conference on Web Search and Data Mining 2022

[C36] Directed Network Embedding with Virtual Negative Edges

Hyunsik Yoo*, Yeon-Chang Lee*, Kijung Shin, and Sang-Wook Kim

WSDM 2022: ACM International Conference on Web Search and Data Mining 2022

[J13] Growth Patterns and Models of Real-world Hypergraphs

Jihoon Ko*, Yunbum Kook*, and Kijung Shin

Knowledge and Information Systems

[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

[J11] Simple Epidemic Models with Segmentation Can Be Better than Complex Ones

Geon Lee, Se-eun Yoon, and Kijung Shin

PLOS ONE

[J10] Real-Time Anomaly Detection in Edge Streams

Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos 

ACM TKDD: ACM Transactions on Knowledge Discovery from Data

[C35] THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting

Geon Lee and Kijung Shin

ICDM 2021: IEEE International Conference on Data Mining 2021

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: International Conference on Medical Image Computing and Computer Assisted Intervention 2021

[C33] SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams

Taehyung Kwon*, Inkyu Park*, Dongjin Lee, and Kijung Shin

ICDE 2021: IEEE International Conference on Data Engineering 2021

[C32] Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers

Dongjin Lee and Kijung Shin

ICDE 2021: IEEE International Conference on Data Engineering 2021

[C31] How Do Hyperedges Overlap in Real-World Hypergraphs? - Patterns, Measures, and Generators

Geon Lee*, Minyoung Choe*, and Kijung Shin

WWW 2021: The Web Conference 2021

[C30] PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation

Minseok Kim, Hwanjun Song, Doyoung Kim, Kijung Shin, and Jae-Gil Lee

AAAI 2021: AAAI Conference on Artificial Intelligence 2021

[C29] DPGS: Degree-Preserving Graph Summarization

Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, Huawei Shen, and Xueqi Cheng

SDM 2021: SIAM International Conference on Data Mining 2021

[C28] MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks

Jihoon Ko, Kyuhan Lee, Kijung Shin, and Noseong Park

ASONAM 2020: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2020

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: IEEE International Conference on Data Mining 2020

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: International Conference on Very Large Data Bases 

[C25]  Incremental Lossless Graph Summarization

Jihoon Ko*, Yunbum Kook*, and Kijung Shin 

KDD 2020: ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2020

[C24]  SSumM: Sparse Summarization of Massive Graphs

Kyuhan Lee*, Hyeonsoo Jo*, Jihoon Ko, Sungsu Lim, and Kijung Shin 

KDD 2020: ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2020

[C23]  Structural Patterns and Generative Models of Real-world Hypergraphs

Manh Tuan Do, Se-eun Yoon, Bryan Hooi, and Kijung Shin

KDD 2020: ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2020

[C22] How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction

Se-eun Yoon, Hyungseok Song, Kijung Shin, and Yung Yi

WWW 2020 (Short Paper): The Web Conference 2020

[C21]  TellTail: Fast Scoring and Detection of Dense Subgraphs

Bryan Hooi, Kijung Shin, Hemank Lamba, and Christos Faloutsos

AAAI 2020: AAAI Conference on Artificial Intelligence 2020

[C20]  MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams 

Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos

AAAI 2020: AAAI Conference on Artificial Intelligence 2020

[J8]  Temporal Locality-Aware Sampling for Accurate Triangle Counting in Real Graph Streams

Dongjin Lee, Kijung Shin, and Christos Faloutsos 

The VLDB Journal

[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  

[C19]  Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach

Minji Yoon, Bryan Hooi, Kijung Shin, and Christos Faloutsos

KDD 2019: ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2019

[C18]  SWeG: Lossless and Lossy Summarization of Web-Scale Graphs

Kijung Shin, Amol Ghoting, Myunghwan Kim, and Hema Raghavan

WWW 2019: The Web Conference 2019

[C16]  Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions

Kijung Shin, Jisu Kim, Bryan Hooi, and Christos Faloutsos

PKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018

[C15] ONE-M: Modeling the Co-evolution of Opinions and Network Connections

Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen,  Boleslaw Szymanski, Christos Faloutsos, and Nitesh Chawla 

PKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018

[C14] Discovering Progression Stages in Trillion-Scale Behavior Logs

Kijung Shin, Mahdi Shafiei, Myunghwan Kim, Aastha Jain, and Hema Raghavan

WWW 2018: The Web Conference 2018

[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: Pacific-Asia Conference on Knowledge Discovery and Data Mining 2018

[J5] Fast, Accurate and Flexible Algorithms for Dense Subtensor Mining

Kijung Shin, Bryan Hooi, and Christos Faloutsos

ACM TKDD: ACM Transactions on Knowledge Discovery from Data

[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  

Included in courses: MIT (6.886)

[C12] WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams

Kijung Shin

ICDM 2017: IEEE International Conference on Data Mining 2017

[C11] ZooRank: Ranking Suspicious Entities in Time-Evolving Tensors

Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, and Jürgen Pfeffer

PKDD 2017: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2017

[C10] DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams

Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos

KDD 2017: ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2017

[C9] Why You Should Charge Your Friends for Borrowing Your Stuff

Kijung Shin, Euiwoong Lee, Dhivya Eswaran, and Ariel D. Procaccia

IJCAI 2017: International Joint Conference on Artificial Intelligence 2017

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: ACM International Conference on Web Search and Data Mining 2017

[C7] S-HOT: Scalable High-Order Tucker Decomposition

Jinoh Oh, Kijung Shin, Evangelos E. Papalexakis, Christos Faloutsos, and Hwanjo Yu

 WSDM 2017: ACM International Conference on Web Search and Data Mining 2017

[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: ACM Transactions on Knowledge Discovery from Data

Special Issue on the Best Papers from KDD 2016

[J2] Fully Scalable Methods for Distributed Tensor Factorization

Kijung Shin, Lee Sael, and U Kang

IEEE TKDE: IEEE Transactions on Knowledge and Data Engineering

[C6] CoreScope: Graph Mining Using k-Core Analysis - Patterns, Anomalies and Algorithms

Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos

ICDM 2016: IEEE International Conference on Data Mining 2016

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: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2016

[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: ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2016 

Received the SIGKDD Best Research Paper Award [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: ACM Transactions on Database Systems

[C3] BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs

Kijung Shin, Jinhong Jung, Lee Sael, and U Kang

SIGMOD 2015: ACM SIGMOD International Conference on the Management of Data 2015

Received the Samsung Humantech Paper Award (1st in Computer Science)

Included in courses: UMich (EECS 598)