Seguir
Kijung Shin
Kijung Shin
Associate Professor, KAIST
Dirección de correo verificada de kaist.ac.kr - Página principal
Título
Citado por
Citado por
Año
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
B Hooi, HA Song, A Beutel, N Shah, K Shin, C Faloutsos
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016
3712016
CoreScope: Graph Mining Using k-Core Analysis—Patterns, Anomalies and Algorithms
K Shin, T Eliassi-Rad, C Faloutsos
2016 IEEE 16th International Conference on Data Mining (ICDM), 469-478, 2016
1422016
M-zoom: Fast dense-block detection in tensors with quality guarantees
K Shin, B Hooi, C Faloutsos
Joint european conference on machine learning and knowledge discovery in …, 2016
1122016
Midas: microcluster-based detector of anomalies in edge streams
S Bhatia, B Hooi, M Yoon, K Shin, C Faloutsos
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3242-3249, 2020
1022020
BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs
K Shin, J Jung, S Lee, U Kang
Proceedings of the 2015 ACM SIGMOD International Conference on Management of …, 2015
942015
Fully Scalable Methods for Distributed Tensor Factorization
K Shin, L Sael, U Kang
IEEE Transactions on Knowledge and Data Engineering 29 (1), 100-113, 2017
892017
Graph-based fraud detection in the face of camouflage
B Hooi, K Shin, HA Song, A Beutel, N Shah, C Faloutsos
ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (4), 1-26, 2017
772017
Distributed methods for high-dimensional and large-scale tensor factorization
K Shin, U Kang
2014 IEEE International Conference on Data Mining, 989-994, 2014
772014
Patterns and anomalies in k-cores of real-world graphs with applications
K Shin, T Eliassi-Rad, C Faloutsos
Knowledge and Information Systems 54 (3), 677-710, 2018
762018
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
M Yoon, B Hooi, K Shin, C Faloutsos
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
752019
D-Cube: Dense-Block Detection in Terabyte-Scale Tensors
K Shin, B Hooi, J Kim, C Faloutsos
Proceedings of the Tenth ACM International Conference on Web Search and Data …, 2017
742017
How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction
S Yoon, H Song, K Shin, Y Yi
Proceedings of The Web Conference 2020, 2627-2633, 2020
722020
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams
K Shin, B Hooi, J Kim, C Faloutsos
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017
722017
Structural Patterns and Generative Models of Real-world Hypergraphs
MT Do, S Yoon, B Hooi, K Shin
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
692020
Hypergraph Motifs: Concepts, Algorithms, and Discoveries
G Lee, J Ko, K Shin
Proceedings of the VLDB Endowment 13 (11), 2256-2269, 2020
682020
SWeG: Lossless and Lossy Summarization of Web-Scale Graphs
K Shin, A Ghoting, M Kim, H Raghavan
The World Wide Web Conference, 1679-1690, 2019
532019
How do hyperedges overlap in real-world hypergraphs?-patterns, measures, and generators
G Lee, M Choe, K Shin
Proceedings of the Web Conference 2021, 3396-3407, 2021
512021
S-HOT: Scalable High-Order Tucker Decomposition
J Oh, K Shin, EE Papalexakis, C Faloutsos, H Yu
Proceedings of the Tenth ACM International Conference on Web Search and Data …, 2017
512017
Random walk with restart on large graphs using block elimination
J Jung, K Shin, L Sael, U Kang
ACM Transactions on Database Systems (TODS) 41 (2), 1-43, 2016
462016
Incremental Lossless Graph Summarization
J Ko, Y Kook, K Shin
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
432020
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20