Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression. - Dongyeop Kang; Woosang Lim; Kijung Shin; Lee Sael; U Kang (CIKM, 2014)

Distributed Methods for High-Dimensional and Large-Scale Tensor Factorization. - Kijung Shin; U Kang (ICDM, 2014)

BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs. - Kijung Shin; Jinhong Jung; Lee Sael; U Kang (SIGMOD Conference, 2015)

CoreScope: Graph Mining Using k-Core Analysis - Patterns, Anomalies and Algorithms. - Kijung Shin; Tina Eliassi-Rad; Christos Faloutsos (ICDM, 2016)

FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. - Bryan Hooi; Hyun Ah Song; Alex Beutel; Neil Shah; Kijung Shin; Christos Faloutsos (KDD, 2016)

M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees. - Kijung Shin; Bryan Hooi; Christos Faloutsos (ECML/PKDD (1), 2016)

Incorporating Side Information in Tensor Completion. - Hemank Lamba; Vaishnavh Nagarajan; Kijung Shin; Naji Shajarisales (WWW (Companion Volume), 2016)

Random Walk with Restart on Large Graphs Using Block Elimination. - Jinhong Jung; Kijung Shin; Lee Sael; U Kang (ACM Trans. Database Syst., 2016)

WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams. - Kijung Shin (ICDM, 2017)

Why You Should Charge Your Friends for Borrowing Your Stuff. - Kijung Shin; Euiwoong Lee; Dhivya Eswaran; Ariel D. Procaccia (IJCAI, 2017)

DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams. - Kijung Shin; Bryan Hooi; Jisu Kim; Christos Faloutsos (KDD, 2017)

zooRank: Ranking Suspicious Entities in Time-Evolving Tensors. - Hemank Lamba; Bryan Hooi; Kijung Shin; Christos Faloutsos; Jürgen Pfeffer (ECML/PKDD (1), 2017)

S-HOT: Scalable High-Order Tucker Decomposition. - Jinoh Oh; Kijung Shin; Evangelos E. Papalexakis; Christos Faloutsos; Hwanjo Yu (WSDM, 2017)

D-Cube: Dense-Block Detection in Terabyte-Scale Tensors. - Kijung Shin; Bryan Hooi; Jisu Kim; Christos Faloutsos (WSDM, 2017)

Graph-Based Fraud Detection in the Face of Camouflage. - Bryan Hooi; Kijung Shin; Hyun Ah Song; Alex Beutel; Neil Shah; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2017)

Fully Scalable Methods for Distributed Tensor Factorization. - Kijung Shin; Lee Sael; U Kang (IEEE Trans. Knowl. Data Eng., 2017)

Tri-Fly: Distributed Estimation of Global and Local Triangle Counts in Graph Streams. - Kijung Shin; Mohammad Hammoud; Euiwoong Lee; Jinoh Oh; Christos Faloutsos (PAKDD (3), 2018)

ONE-M: Modeling the Co-evolution of Opinions and Network Connections. - Aastha Nigam; Kijung Shin; Ashwin Bahulkar; Bryan Hooi; David Hachen; Boleslaw K. Szymanski; Christos Faloutsos; Nitesh V. Chawla (ECML/PKDD (2), 2018)

Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions. - Kijung Shin; Jisu Kim; Bryan Hooi; Christos Faloutsos (ECML/PKDD (2), 2018)

Discovering Progression Stages in Trillion-Scale Behavior Logs. - Kijung Shin; Mahdi Shafiei; Myunghwan Kim 002; Aastha Jain; Hema Raghavan (WWW, 2018)

Out-of-Core and Distributed Algorithms for Dense Subtensor Mining. - Kijung Shin; Bryan Hooi; Jisu Kim; Christos Faloutsos (CoRR, 2018)

DiSLR: Distributed Sampling with Limited Redundancy For Triangle Counting in Graph Streams. - Kijung Shin; Euiwoong Lee; Jinoh Oh; Mohammad Hammoud; Christos Faloutsos (CoRR, 2018)

Patterns and anomalies in k-cores of real-world graphs with applications. - Kijung Shin; Tina Eliassi-Rad; Christos Faloutsos (Knowl. Inf. Syst., 2018)

Fast, Accurate, and Flexible Algorithms for Dense Subtensor Mining. - Kijung Shin; Bryan Hooi; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2018)

Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach. - Minji Yoon; Bryan Hooi; Kijung Shin; Christos Faloutsos (KDD, 2019)

SMF: Drift-Aware Matrix Factorization with Seasonal Patterns. - Bryan Hooi; Kijung Shin; Shenghua Liu; Christos Faloutsos (SDM, 2019)

SWeG: Lossless and Lossy Summarization of Web-Scale Graphs. - Kijung Shin; Amol Ghoting; Myunghwan Kim 002; Hema Raghavan (WWW, 2019)

Midas: Microcluster-Based Detector of Anomalies in Edge Streams. - Siddharth Bhatia 001; Bryan Hooi; Minji Yoon; Kijung Shin; Christos Faloutsos (AAAI, 2020)

TellTail: Fast Scoring and Detection of Dense Subgraphs. - Bryan Hooi; Kijung Shin; Hemank Lamba; Christos Faloutsos (AAAI, 2020)

MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks. - Jihoon Ko; Kyuhan Lee; Kijung Shin; Noseong Park (ASONAM, 2020)

Evolution of Real-world Hypergraphs: Patterns and Models without Oracles. - Yunbum Kook; Jihoon Ko; Kijung Shin (ICDM, 2020)

Structural Patterns and Generative Models of Real-world Hypergraphs. - Manh Tuan Do; Se-eun Yoon; Bryan Hooi; Kijung Shin (KDD, 2020)

Incremental Lossless Graph Summarization. - Jihoon Ko; Yunbum Kook; Kijung Shin (KDD, 2020)

SSumM: Sparse Summarization of Massive Graphs. - Kyuhan Lee; Hyeonsoo Jo; Jihoon Ko; Sungsu Lim; Kijung Shin (KDD, 2020)

How Much and When Do We Need Higher-order Informationin Hypergraphs? A Case Study on Hyperedge Prediction. - Se-eun Yoon; HyungSeok Song; Kijung Shin; Yung Yi (WWW, 2020)

MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Social Networks. - Jihoon Ko; Kyuhan Lee; Kijung Shin; Noseong Park (CoRR, 2020)

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; Yung Yi (CoRR, 2020)

Real-Time Streaming Anomaly Detection in Dynamic Graphs. - Siddharth Bhatia 001; Rui Liu 004; Bryan Hooi; Minji Yoon; Kijung Shin; Christos Faloutsos (CoRR, 2020)

Summarizing graphs using the configuration model. - Houquan Zhou 002; Shenghua Liu; Kyuhan Lee; Kijung Shin; Huawei Shen; Xueqi Cheng (CoRR, 2020)

Fast and memory-efficient algorithms for high-order Tucker decomposition. - Jiyuan Zhang; Jinoh Oh; Kijung Shin; Evangelos E. Papalexakis; Christos Faloutsos; Hwanjo Yu (Knowl. Inf. Syst., 2020)

Hypergraph Motifs: Concepts, Algorithms, and Discoveries. - Geon Lee; Jihoon Ko; Kijung Shin (Proc. VLDB Endow., 2020)

Fast, Accurate and Provable Triangle Counting in Fully Dynamic Graph Streams. - Kijung Shin; Sejoon Oh; Jisu Kim; Bryan Hooi; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2020)

Temporal locality-aware sampling for accurate triangle counting in real graph streams. - Dongjin Lee; Kijung Shin; Christos Faloutsos (VLDB J., 2020)

PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation. - Minseok Kim; Hwanjun Song; Doyoung Kim; Kijung Shin; Jae-Gil Lee 001 (AAAI, 2021)

SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams. - Taehyung Kwon; Inkyu Park 001; Dongjin Lee; Kijung Shin (ICDE, 2021)

Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers. - Dongjin Lee; Kijung Shin (ICDE, 2021)

THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting. - Geon Lee; Kijung Shin (ICDM, 2021)

Efficient Neural Network Approximation of Robust PCA for Automated Analysis of Calcium Imaging Data. - Seungjae Han; Eun-Seo Cho; Inkyu Park 001; Kijung Shin; Young-Gyu Yoon (MICCAI (7), 2021)

Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts. - Aleksandra Gruca; Federico Serva; Llorenç Lliso; Pilar Rípodas; Xavier Calbet; Pedro Herruzo; Jirí Pihrt; Rudolf Raevskiy; Petr Simánek; Matej Choma; Yang Li; Haiyu Dong; et al. (NeurIPS (Competition and Demos), 2021)

DPGS: Degree-Preserving Graph Summarization. - Houquan Zhou 002; Shenghua Liu; Kyuhan Lee; Kijung Shin; Huawei Shen; Xueqi Cheng (SDM, 2021)

How Do Hyperedges Overlap in Real-World Hypergraphs? - Patterns, Measures, and Generators. - Geon Lee; Minyoung Choe; Kijung Shin (WWW, 2021)

Learning to Pool in Graph Neural Networks for Extrapolation. - Jihoon Ko; Taehyung Kwon; Kijung Shin; Juho Lee 001 (CoRR, 2021)

CoCoS: Fast and Accurate Distributed Triangle Counting in Graph Streams. - Kijung Shin; Euiwoong Lee; Jinoh Oh; Mohammad Hammoud; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2021)

Meta-Learning for Online Update of Recommender Systems. - Minseok Kim; Hwanjun Song; Yooju Shin; Dongmin Park; Kijung Shin; Jae-Gil Lee 001 (AAAI, 2022)

MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation. - Taeri Kim; Yeon-Chang Lee; Kijung Shin; Sang-Wook Kim (CIKM, 2022)

Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. - Geon Lee; Jaemin Yoo; Kijung Shin (CIKM, 2022)

Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications. - Shinhwan Kang; Kyuhan Lee; Kijung Shin (ICDE, 2022)

SLUGGER: Lossless Hierarchical Summarization of Massive Graphs. - Kyuhan Lee; Jihoon Ko; Kijung Shin (ICDE, 2022)

Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data. - Jihoon Ko; Kyuhan Lee; Hyunjin Hwang; Kijung Shin (ICDM (Workshops), 2022)

Set2Box: Similarity Preserving Representation Learning for Sets. - Geon Lee; Chanyoung Park 001; Kijung Shin (ICDM, 2022)

HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams. - Geon Lee; Minyoung Choe; Kijung Shin (IJCAI, 2022)

Are Edge Weights in Summary Graphs Useful? - A Comparative Study. - Shinhwan Kang; Kyuhan Lee; Kijung Shin (PAKDD (1), 2022)

On the Persistence of Higher-Order Interactions in Real-World Hypergraphs. - Hyunjin Choo; Kijung Shin (SDM, 2022)

AHP: Learning to Negative Sample for Hyperedge Prediction. - Hyunjin Hwang; Seungwoo Lee; Chanyoung Park 001; Kijung Shin (SIGIR, 2022)

Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs. - Hyeonjeong Shin; Taehyung Kwon; Neil Shah; Kijung Shin (WSDM, 2022)

Directed Network Embedding with Virtual Negative Edges. - Hyunsik Yoo; Yeon-Chang Lee; Kijung Shin; Sang-Wook Kim (WSDM, 2022)

MiDaS: Representative Sampling from Real-world Hypergraphs. - Minyoung Choe; Jaemin Yoo; Geon Lee; Woonsung Baek; U Kang; Kijung Shin (WWW, 2022)

BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning. - Jihoon Ko; Shinhwan Kang; Kijung Shin (CoRR, 2022)

Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition. - Taehyeon Kim 001; Shinhwan Kang; Hyeonjeong Shin; Deukryeol Yoon; Seongha Eom; Kijung Shin; Se-Young Yun (CoRR, 2022)

Effective training strategies for deep-learning-based precipitation nowcasting and estimation. - Jihoon Ko; Kyuhan Lee; Hyunjin Hwang; Seok-Geun Oh; Seok-Woo Son; Kijung Shin (Comput. Geosci., 2022)

Growth patterns and models of real-world hypergraphs. - Jihoon Ko; Yunbum Kook; Kijung Shin (Knowl. Inf. Syst., 2022)

Real-Time Anomaly Detection in Edge Streams. - Siddharth Bhatia 001; Rui Liu 004; Bryan Hooi; Minji Yoon; Kijung Shin; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2022)

I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs. - Dongjin Lee; Kijung Shin (AAAI, 2023)

Robust Graph Clustering via Meta Weighting for Noisy Graphs. - Hyeonsoo Jo; Fanchen Bu; Kijung Shin (CIKM, 2023)

You're Not Alone in Battle: Combat Threat Analysis Using Attention Networks and a New Open Benchmark. - Soo Yong Lee; Juwon Kim; Kiwoong Park; Dong Kuk Ryu; Sang Heun Shim; Kijung Shin (CIKM, 2023)

TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions. - Taehyung Kwon; Jihoon Ko; Jinhong Jung; Kijung Shin (ICDM, 2023)

Towards Deep Attention in Graph Neural Networks: Problems and Remedies. - Soo Yong Lee; Fanchen Bu; Jaemin Yoo; Kijung Shin (ICML, 2023)

On Improving the Cohesiveness of Graphs by Merging Nodes: Formulation, Analysis, and Algorithms. - Fanchen Bu; Kijung Shin (KDD, 2023)

Classification of Edge-dependent Labels of Nodes in Hypergraphs. - Minyoung Choe; Sunwoo Kim 006; Jaemin Yoo; Kijung Shin (KDD, 2023)

How Transitive Are Real-World Group Interactions? - Measurement and Reproduction. - Sunwoo Kim 006; Fanchen Bu; Minyoung Choe; Jaemin Yoo; Kijung Shin (KDD, 2023)

Robust and Efficient Alignment of Calcium Imaging Data through Simultaneous Low Rank and Sparse Decomposition. - Junmo Cho; Seungjae Han; Eun-Seo Cho; Kijung Shin; Young-Gyu Yoon (WACV, 2023)

Tutorials at The Web Conference 2023. - Valeria Fionda; Olaf Hartig; Reyhaneh Abdolazimi; Sihem Amer-Yahia; Hongzhi Chen; Xiao Chen 016; Peng Cui 001; Jeffrey Dalton 001; Xin Luna Dong; Lisette Espín-Noboa; Wenqi Fan; Manuela Fritz; et al. (WWW (Companion Volume), 2023)

Characterization of Simplicial Complexes by Counting Simplets Beyond Four Nodes. - Hyunju Kim; Jihoon Ko; Fanchen Bu; Kijung Shin (WWW, 2023)

NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors. - Taehyung Kwon; Jihoon Ko; Jinhong Jung; Kijung Shin (WWW, 2023)

Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding. - Hyunsik Yoo; Yeon-Chang Lee; Kijung Shin; Sang-Wook Kim (WWW, 2023)

Graphlets over Time: A New Lens for Temporal Network Analysis. - Deukryeol Yoon; Dongjin Lee; Minyoung Choe; Kijung Shin (CoRR, 2023)

Four-set Hypergraphlets for Characterization of Directed Hypergraphs. - Heechan Moon; Hyunju Kim; Sunwoo Kim 006; Kijung Shin (CoRR, 2023)

Temporal hypergraph motifs. - Geon Lee; Kijung Shin (Knowl. Inf. Syst., 2023)

Two-Stage Training of Graph Neural Networks for Graph Classification. - Manh Tuan Do; Noseong Park; Kijung Shin (Neural Process. Lett., 2023)

Interplay between topology and edge weights in real-world graphs: concepts, patterns, and an algorithm. - Fanchen Bu; Shinhwan Kang; Kijung Shin (Data Min. Knowl. Discov., 2023)

Hypercore decomposition for non-fragile hyperedges: concepts, algorithms, observations, and applications. - Fanchen Bu; Geon Lee; Kijung Shin (Data Min. Knowl. Discov., 2023)

Improving the core resilience of real-world hypergraphs. - Manh Tuan Do; Kijung Shin (Data Min. Knowl. Discov., 2023)

Reciprocity in directed hypergraphs: measures, findings, and generators. - Sunwoo Kim 006; Minyoung Choe; Jaemin Yoo; Kijung Shin (Data Min. Knowl. Discov., 2023)

Datasets, tasks, and training methods for large-scale hypergraph learning. - Sunwoo Kim 006; Dongjin Lee; Yul Kim; Jungho Park; Taeho Hwang; Kijung Shin (Data Min. Knowl. Discov., 2023)

VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation. - Taeri Kim; Jiho Heo; Hongil Kim; Kijung Shin; Sang-Wook Kim (AAAI, 2024)

Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs. - Dongjin Lee; Juho Lee 001; Kijung Shin (AAAI, 2024)

Towards Better Utilization of Multiple Views for Bundle Recommendation. - Kyungho Kim; Sunwoo Kim 006; Geon Lee; Kijung Shin (CIKM, 2024)

Post-Training Embedding Enhancement for Long-Tail Recommendation. - Geon Lee; Kyungho Kim; Kijung Shin (CIKM, 2024)

FlowerFormer: Empowering Neural Architecture Encoding Using a Flow-Aware Graph Transformer. - Dongyeong Hwang; Hyunju Kim; Sunwoo Kim 006; Kijung Shin (CVPR, 2024)

Resource2Box: Learning To Rank Resources in Distributed Search Using Box Embedding. - Ulugbek Ergashev; Geon Lee; Kijung Shin; Eduard C. Dragut; Weiyi Meng (ICDM, 2024)

ELiCiT: Effective and Lightweight Lossy Compression of Tensors. - Jihoon Ko; Taehyung Kwon; Jinhong Jung; Kijung Shin (ICDM, 2024)

Prediction Is NOT Classification: On Formulation and Evaluation of Hyperedge Prediction. - Taehyung Yu; Soo Yong Lee; Hyunjin Hwang; Kijung Shin (ICDM (Workshops), 2024)

HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs. - Sunwoo Kim 006; Shinhwan Kang; Fanchen Bu; Soo Yong Lee; Jaemin Yoo; Kijung Shin (ICLR, 2024)

Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More. - Fanchen Bu; Hyeonsoo Jo; Soo Yong Lee; Sungsoo Ahn; Kijung Shin (ICML, 2024)

Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective. - Soo Yong Lee; Sunwoo Kim 006; Fanchen Bu; Jaemin Yoo; Jiliang Tang; Kijung Shin (ICML, 2024)

Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs. - Langzhang Liang; Sunwoo Kim 006; Kijung Shin; Zenglin Xu; Shirui Pan; Yuan Qi 001 (ICML, 2024)

Unsupervised Alignment of Hypergraphs with Different Scales. - Manh Tuan Do; Kijung Shin (KDD, 2024)

A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. - Sunwoo Kim 006; Soo Yong Lee; Yue Gao 002; Alessia Antelmi; Mirko Polato; Kijung Shin (KDD, 2024)

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; Kijung Shin (KDD, 2024)

SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning. - Jongha Lee; Sunwoo Kim 006; Kijung Shin (KDD, 2024)

Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy. - Sunwoo Kim 006; Soo Yong Lee; Fanchen Bu; Shinhwan Kang; Kyungho Kim; Jaemin Yoo; Kijung Shin (NeurIPS, 2024)

Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation. - Geon Lee; Kyungho Kim; Kijung Shin (RecSys, 2024)

Self-Guided Robust Graph Structure Refinement. - Yeonjun In; Kanghoon Yoon; Kibum Kim; Kijung Shin; Chanyoung Park 001 (WWW, 2024)

VilLain: Self-Supervised Learning on Homogeneous Hypergraphs without Features via Virtual Label Propagation. - Geon Lee; Soo Yong Lee; Kijung Shin (WWW, 2024)

Exploring Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms. - Fanchen Bu; Ruochen Yang; Paul Bogdan; Kijung Shin (CoRR, 2024)

Random walk with restart on hypergraphs: fast computation and an application to anomaly detection. - Jaewan Chun; Geon Lee; Kijung Shin; Jinhong Jung (Data Min. Knowl. Discov., 2024)

Hypergraph motifs and their extensions beyond binary. - Geon Lee; Seokbum Yoon; Jihoon Ko; Hyunju Kim; Kijung Shin (VLDB J., 2024)

Representative and Back-In-Time Sampling from Real-world Hypergraphs. - Minyoung Choe; Jaemin Yoo; Geon Lee; Woonsung Baek; U Kang; Kijung Shin (ACM Trans. Knowl. Discov. Data, 2024)

DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation. - Junghun Lee; Hyunju Kim; Fanchen Bu; Jihoon Ko; Kijung Shin (AAAI, 2025)

TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents. - Geon Lee; Wenchao Yu; Kijung Shin; Wei Cheng 002; Haifeng Chen (AAAI, 2025)

RASP: Robust Mining of Frequent Temporal Sequential Patterns under Temporal Variations. - Hyunjin Choo; Minho Eom; Gyuri Kim; Young-Gyu Yoon; Kijung Shin (EDBT, 2025)

Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts. - Jongha Lee; Taehyung Kwon; Heechan Moon; Kijung Shin (ICDE, 2025)

MARIOH: Multiplicity-Aware Hypergraph Reconstruction. - Kyuhan Lee; Geon Lee; Kijung Shin (ICDE, 2025)

On Measuring Unnoticeability of Graph Adversarial Attacks: Observations, New Measure, and Applications. - Hyeonsoo Jo; Hyunjin Hwang; Fanchen Bu; Soo Yong Lee; Chanyoung Park 001; Kijung Shin (KDD (1), 2025)

TiGer: Self-supervised Purification for Time-Evolving Graphs. - Hyeonsoo Jo; Jongha Lee; Fanchen Bu; Kijung Shin (PAKDD (2), 2025)

Multi-behavior Recommender Systems: A Survey. - Kyungho Kim; Sunwoo Kim 006; Geon Lee; Jinhong Jung; Kijung Shin (PAKDD (4), 2025)

KGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking. - Juyeon Kim; Geon Lee; Taeuk Kim; Kijung Shin (SIGIR, 2025)

Kronecker Generative Models for Power-Law Patterns in Real-World Hypergraphs. - Minyoung Choe; Jihoon Ko; Taehyung Kwon; Kijung Shin; Christos Faloutsos (WWW, 2025)

Beyond Neighbors: Distance-Generalized Graphlets for Enhanced Graph Characterization. - Yeongho Kim; Yuyeong Kim; Geon Lee; Kijung Shin (WWW, 2025)

Gene expression inference based on graph neural networks using L1000 data. - Tae Hyun Kim; Harim Kim; Hyunjin Hwang; Shinwhan Kang; Kijung Shin; Inwha Baek (Briefings Bioinform., 2025)

Compact lossy compression of tensors via neural tensor-train decomposition. - Taehyung Kwon; Jihoon Ko; Jinhong Jung; Jun-Gi Jang; Kijung Shin (Knowl. Inf. Syst., 2025)

BeGin: Extensive Benchmark Scenarios and an Easy-to-use Framework for Graph Continual Learning. - Jihoon Ko; Shinhwan Kang; Taehyung Kwon; Heechan Moon; Kijung Shin (ACM Trans. Intell. Syst. Technol., 2025)

Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop. - Yushan Jiang; Wenchao Yu; Geon Lee; Dongjin Song; Kijung Shin; Wei Cheng 002; Yanchi Liu; Haifeng Chen (CoRR, 2025)

Estimating simplet counts via sampling. - Hyunju Kim; Heechan Moon; Fanchen Bu; Jihoon Ko; Kijung Shin (VLDB J., 2025)

Emergence of psychopathological computations in large language models. - Soo Yong Lee; Hyunjin Hwang; Taekwan Kim; Yuyeong Kim; Kyuri Park; Jaemin Yoo; Denny Borsboom; Kijung Shin (CoRR, 2025)

'Hello, World!': Making GNNs Talk with LLMs. - Sunwoo Kim; Soo Yong Lee; Jaemin Yoo; Kijung Shin (CoRR, 2025)

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 001; Kijung Shin (CoRR, 2025)

Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification. - Langzhang Liang; Fanchen Bu; Zixing Song; Zenglin Xu; Shirui Pan; Kijung Shin (CoRR, 2025)

On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and Analysis. - Fanchen Bu; Kijung Shin (CoRR, 2025)

PyTorch-based Geometric Learning with Non-CUDA Processing Units: Experiences from Intel Gaudi-v2 HPUs. - Fanchen Bu; Kijung Shin (CoRR, 2025)

A Survey on Hypergraph Mining: Patterns, Tools, and Generators. - Geon Lee; Fanchen Bu; Tina Eliassi-Rad; Kijung Shin (ACM Comput. Surv., 2025)

Effective and lightweight lossy compression of tensors: techniques and applications. - Jihoon Ko; Taehyung Kwon; Jinhong Jung; Kijung Shin (Knowl. Inf. Syst., 2025)