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2020 – today
- 2023
[j14]Geon Lee, Kijung Shin
:
Temporal hypergraph motifs. Knowl. Inf. Syst. 65(4): 1549-1586 (2023)- [c51]Junmo Cho, Seungjae Han, Eun-Seo Cho, Kijung Shin, Young-Gyu Yoon:
Robust and Efficient Alignment of Calcium Imaging Data through Simultaneous Low Rank and Sparse Decomposition. WACV 2023: 1938-1947 - [i43]Deukryeol Yoon, Dongjin Lee, Minyoung Choe, Kijung Shin:
Graphlets over Time: A New Lens for Temporal Network Analysis. CoRR abs/2301.00310 (2023) - [i42]Fanchen Bu, Geon Lee, Kijung Shin:
Hypercore Decomposition for Non-Fragile Hyperedges: Concepts, Algorithms, Observations, and Applications. CoRR abs/2301.08440 (2023) - [i41]Taehyung Kwon, Jihoon Ko, Jinhong Jung, Kijung Shin:
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors. CoRR abs/2302.04570 (2023) - [i40]Hyunju Kim, Jihoon Ko, Fanchen Bu, Kijung Shin:
Characterization of Simplicial Complexes by Counting Simplets Beyond Four Nodes. CoRR abs/2302.05505 (2023) - 2022
- [j13]Jihoon Ko, Kyuhan Lee, Hyunjin Hwang, Seok-Geun Oh, Seok-Woo Son, Kijung Shin:
Effective training strategies for deep-learning-based precipitation nowcasting and estimation. Comput. Geosci. 161: 105072 (2022) - [j12]Jihoon Ko, Yunbum Kook, Kijung Shin:
Growth patterns and models of real-world hypergraphs. Knowl. Inf. Syst. 64(11): 2883-2920 (2022) - [j11]Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos:
Real-Time Anomaly Detection in Edge Streams. ACM Trans. Knowl. Discov. Data 16(4): 75:1-75:22 (2022) - [c50]Minseok Kim, Hwanjun Song, Yooju Shin, Dongmin Park, Kijung Shin, Jae-Gil Lee:
Meta-Learning for Online Update of Recommender Systems. AAAI 2022: 4065-4074 - [c49]Taeri Kim, Yeon-Chang Lee, Kijung Shin, Sang-Wook Kim:
MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation. CIKM 2022: 993-1002 - [c48]Geon Lee, Jaemin Yoo, Kijung Shin:
Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. CIKM 2022: 5144-5147 - [c47]Kyuhan Lee, Jihoon Ko, Kijung Shin:
SLUGGER: Lossless Hierarchical Summarization of Massive Graphs. ICDE 2022: 472-484 - [c46]Shinhwan Kang, Kyuhan Lee, Kijung Shin:
Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications. ICDE 2022: 2319-2332 - [c45]Sunwoo Kim, Minyoung Choe, Jaemin Yoo, Kijung Shin:
Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators. ICDM 2022: 1005-1010 - [c44]Geon Lee, Chanyoung Park, Kijung Shin:
Set2Box: Similarity Preserving Representation Learning for Sets. ICDM 2022: 1023-1028 - [c43]Jihoon Ko, Kyuhan Lee, Hyunjin Hwang, Kijung Shin:
Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data. ICDM (Workshops) 2022: 1063-1070 - [c42]Geon Lee, Minyoung Choe, Kijung Shin:
HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams. IJCAI 2022: 2129-2137 - [c41]Shinhwan Kang, Kyuhan Lee, Kijung Shin:
Are Edge Weights in Summary Graphs Useful? - A Comparative Study. PAKDD (1) 2022: 54-67 - [c40]Hyunjin Choo, Kijung Shin:
On the Persistence of Higher-Order Interactions in Real-World Hypergraphs. SDM 2022: 163-171 - [c39]Hyunjin Hwang, Seungwoo Lee, Chanyoung Park, Kijung Shin:
AHP: Learning to Negative Sample for Hyperedge Prediction. SIGIR 2022: 2237-2242 - [c38]Hyeonjeong Shin, Taehyung Kwon, Neil Shah, Kijung Shin:
Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs. WSDM 2022: 908-916 - [c37]Hyunsik Yoo, Yeon-Chang Lee, Kijung Shin, Sang-Wook Kim:
Directed Network Embedding with Virtual Negative Edges. WSDM 2022: 1291-1299 - [c36]Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, Kijung Shin:
MiDaS: Representative Sampling from Real-world Hypergraphs. WWW 2022: 1080-1092 - [i39]Hyunjin Choo, Kijung Shin:
On the Persistence of Higher-Order Interactions in Real-World Hypergraphs. CoRR abs/2201.00972 (2022) - [i38]Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, Kijung Shin:
MiDaS: Representative Sampling from Real-world Hypergraphs. CoRR abs/2202.01587 (2022) - [i37]Jihoon Ko, Kyuhan Lee, Hyunjin Hwang, Seok-Geun Oh, Seok-Woo Son, Kijung Shin:
Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimation. CoRR abs/2202.10555 (2022) - [i36]Minseok Kim, Hwanjun Song, Yooju Shin, Dongmin Park, Kijung Shin, Jae-Gil Lee:
Meta-Learning for Online Update of Recommender Systems. CoRR abs/2203.10354 (2022) - [i35]Shinhwan Kang, Kyuhan Lee, Kijung Shin:
Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications. CoRR abs/2203.14755 (2022) - [i34]Shinhwan Kang, Kyuhan Lee, Kijung Shin:
Are Edge Weights in Summary Graphs Useful? - A Comparative Study. CoRR abs/2203.15212 (2022) - [i33]Hyunjin Hwang, Seungwoo Lee, Chanyoung Park, Kijung Shin:
AHP: Learning to Negative Sample for Hyperedge Prediction. CoRR abs/2204.06353 (2022) - [i32]Geon Lee, Minyoung Choe, Kijung Shin:
HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams. CoRR abs/2204.13822 (2022) - [i31]Dongjin Lee, Kijung Shin:
I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs. CoRR abs/2206.04739 (2022) - [i30]Geon Lee, Chanyoung Park, Kijung Shin:
Set2Box: Similarity Preserving Representation Learning of Sets. CoRR abs/2210.03282 (2022) - [i29]Sunwoo Kim, Minyoung Choe, Jaemin Yoo, Kijung Shin:
Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators. CoRR abs/2210.05328 (2022) - [i28]Jihoon Ko, Kyuhan Lee, Hyunjin Hwang, Kijung Shin:
Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data. CoRR abs/2210.12853 (2022) - [i27]Jihoon Ko, Shinhwan Kang, Kijung Shin:
BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning. CoRR abs/2211.14568 (2022) - [i26]Taehyeon Kim, Shinhwan Kang, Hyeonjeong Shin, Deukryeol Yoon, Seongha Eom, Kijung Shin, Se-Young Yun:
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition. CoRR abs/2212.02059 (2022) - 2021
- [j10]Kijung Shin, Euiwoong Lee, Jinoh Oh, Mohammad Hammoud, Christos Faloutsos:
CoCoS: Fast and Accurate Distributed Triangle Counting in Graph Streams. ACM Trans. Knowl. Discov. Data 15(3): 38:1-38:30 (2021) - [c35]Minseok Kim, Hwanjun Song, Doyoung Kim, Kijung Shin, Jae-Gil Lee:
PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation. AAAI 2021: 4164-4171 - [c34]Taehyung Kwon, Inkyu Park, Dongjin Lee, Kijung Shin:
SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams. ICDE 2021: 816-827 - [c33]Dongjin Lee, Kijung Shin:
Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers. ICDE 2021: 840-851 - [c32]Geon Lee, Kijung Shin:
THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting. ICDM 2021: 310-319 - [c31]Seungjae Han, Eun-Seo Cho, Inkyu Park, Kijung Shin, Young-Gyu Yoon:
Efficient Neural Network Approximation of Robust PCA for Automated Analysis of Calcium Imaging Data. MICCAI (7) 2021: 595-604 - [c30]Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, Huawei Shen, Xueqi Cheng:
DPGS: Degree-Preserving Graph Summarization. SDM 2021: 280-288 - [c29]Geon Lee, Minyoung Choe, Kijung Shin:
How Do Hyperedges Overlap in Real-World Hypergraphs? - Patterns, Measures, and Generators. WWW 2021: 3396-3407 - [i25]Geon Lee, Minyoung Choe, Kijung Shin:
How Do Hyperedges Overlap in Real-World Hypergraphs? - Patterns, Measures, and Generators. CoRR abs/2101.07480 (2021) - [i24]Dongjin Lee, Kijung Shin:
Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers. CoRR abs/2102.08466 (2021) - [i23]Taehyung Kwon, Inkyu Park, Dongjin Lee, Kijung Shin:
SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams. CoRR abs/2102.11517 (2021) - [i22]Jihoon Ko, Taehyung Kwon, Kijung Shin, Juho Lee:
Learning to Pool in Graph Neural Networks for Extrapolation. CoRR abs/2106.06210 (2021) - [i21]Geon Lee, Kijung Shin:
THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting. CoRR abs/2109.08341 (2021) - [i20]Hyeonjeong Shin, Taehyung Kwon, Neil Shah, Kijung Shin:
Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs. CoRR abs/2110.14875 (2021) - [i19]Kyuhan Lee, Jihoon Ko, Kijung Shin:
SLUGGER: Lossless Hierarchical Summarization of Massive Graphs. CoRR abs/2112.05374 (2021) - 2020
- [j9]Jiyuan Zhang, Jinoh Oh, Kijung Shin, Evangelos E. Papalexakis, Christos Faloutsos, Hwanjo Yu:
Fast and memory-efficient algorithms for high-order Tucker decomposition. Knowl. Inf. Syst. 62(7): 2765-2794 (2020) - [j8]Geon Lee, Jihoon Ko, Kijung Shin:
Hypergraph Motifs: Concepts, Algorithms, and Discoveries. Proc. VLDB Endow. 13(11): 2256-2269 (2020) - [j7]Kijung Shin, Sejoon Oh, Jisu Kim, Bryan Hooi, Christos Faloutsos:
Fast, Accurate and Provable Triangle Counting in Fully Dynamic Graph Streams. ACM Trans. Knowl. Discov. Data 14(2): 12:1-12:39 (2020) - [j6]Dongjin Lee, Kijung Shin, Christos Faloutsos:
Temporal locality-aware sampling for accurate triangle counting in real graph streams. VLDB J. 29(6): 1501-1525 (2020) - [c28]Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos:
Midas: Microcluster-Based Detector of Anomalies in Edge Streams. AAAI 2020: 3242-3249 - [c27]Bryan Hooi, Kijung Shin, Hemank Lamba, Christos Faloutsos:
TellTail: Fast Scoring and Detection of Dense Subgraphs. AAAI 2020: 4150-4157 - [c26]Jihoon Ko, Kyuhan Lee, Kijung Shin, Noseong Park:
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks. ASONAM 2020: 204-211 - [c25]Yunbum Kook, Jihoon Ko, Kijung Shin:
Evolution of Real-world Hypergraphs: Patterns and Models without Oracles. ICDM 2020: 272-281 - [c24]Kyuhan Lee, Hyeonsoo Jo, Jihoon Ko, Sungsu Lim, Kijung Shin:
SSumM: Sparse Summarization of Massive Graphs. KDD 2020: 144-154 - [c23]Manh Tuan Do, Se-eun Yoon, Bryan Hooi, Kijung Shin:
Structural Patterns and Generative Models of Real-world Hypergraphs. KDD 2020: 176-186 - [c22]Jihoon Ko, Yunbum Kook, Kijung Shin:
Incremental Lossless Graph Summarization. KDD 2020: 317-327 - [c21]Se-eun Yoon, HyungSeok Song, Kijung Shin, Yung Yi:
How Much and When Do We Need Higher-order Informationin Hypergraphs? A Case Study on Hyperedge Prediction. WWW 2020: 2627-2633 - [i18]Jihoon Ko, Kyuhan Lee, Kijung Shin, Noseong Park:
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Social Networks. CoRR abs/2001.08853 (2020) - [i17]Se-eun Yoon, HyungSeok Song, Kijung Shin, Yung Yi:
How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction. CoRR abs/2001.11181 (2020) - [i16]Geon Lee, Jihoon Ko, Kijung Shin:
Hypergraph Motifs: Concepts, Algorithms, and Discoveries. CoRR abs/2003.01853 (2020) - [i15]Kyuhan Lee, Hyeonsoo Jo, Jihoon Ko, Sungsu Lim, Kijung Shin:
SSumM: Sparse Summarization of Massive Graphs. CoRR abs/2006.01060 (2020) - [i14]Manh Tuan Do, Se-eun Yoon, Bryan Hooi, Kijung Shin:
Structural Patterns and Generative Models of Real-world Hypergraphs. CoRR abs/2006.07060 (2020) - [i13]Jihoon Ko, Yunbum Kook, Kijung Shin:
Incremental Lossless Graph Summarization. CoRR abs/2006.09935 (2020) - [i12]Yunbum Kook, Jihoon Ko, Kijung Shin:
Evolution of Real-world Hypergraphs: Patterns and Models without Oracles. CoRR abs/2008.12729 (2020) - [i11]Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos:
Real-Time Streaming Anomaly Detection in Dynamic Graphs. CoRR abs/2009.08452 (2020) - [i10]Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, Huawei Shen, Xueqi Cheng:
Summarizing graphs using the configuration model. CoRR abs/2010.09175 (2020) - [i9]Manh Tuan Do, Noseong Park, Kijung Shin:
Two-stage Training of Graph Neural Networks for Graph Classification. CoRR abs/2011.05097 (2020) - [i8]Minji Yoon, Bryan Hooi, Kijung Shin, Christos Faloutsos:
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach. CoRR abs/2011.13085 (2020)
2010 – 2019
- 2019
- [c20]Minji Yoon, Bryan Hooi, Kijung Shin, Christos Faloutsos:
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach. KDD 2019: 647-657 - [c19]Bryan Hooi, Kijung Shin, Shenghua Liu, Christos Faloutsos:
SMF: Drift-Aware Matrix Factorization with Seasonal Patterns. SDM 2019: 621-629 - [c18]Kijung Shin, Amol Ghoting, Myunghwan Kim, Hema Raghavan:
SWeG: Lossless and Lossy Summarization of Web-Scale Graphs. WWW 2019: 1679-1690 - [i7]Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos:
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams. CoRR abs/1911.04464 (2019) - 2018
- [j5]Kijung Shin, Tina Eliassi-Rad, Christos Faloutsos:
Patterns and anomalies in k-cores of real-world graphs with applications. Knowl. Inf. Syst. 54(3): 677-710 (2018) - [j4]Kijung Shin, Bryan Hooi, Christos Faloutsos:
Fast, Accurate, and Flexible Algorithms for Dense Subtensor Mining. ACM Trans. Knowl. Discov. Data 12(3): 28:1-28:30 (2018) - [c17]Kijung Shin, Mohammad Hammoud, Euiwoong Lee, Jinoh Oh, Christos Faloutsos:
Tri-Fly: Distributed Estimation of Global and Local Triangle Counts in Graph Streams. PAKDD (3) 2018: 651-663 - [c16]Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen, Boleslaw K. Szymanski, Christos Faloutsos, Nitesh V. Chawla:
ONE-M: Modeling the Co-evolution of Opinions and Network Connections. ECML/PKDD (2) 2018: 122-140 - [c15]Kijung Shin, Jisu Kim, Bryan Hooi, Christos Faloutsos:
Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions. ECML/PKDD (2) 2018: 141-157 - [c14]Kijung Shin, Mahdi Shafiei, Myunghwan Kim, Aastha Jain, Hema Raghavan:
Discovering Progression Stages in Trillion-Scale Behavior Logs. WWW 2018: 1765-1774 - [i6]Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos:
Out-of-Core and Distributed Algorithms for Dense Subtensor Mining. CoRR abs/1802.01065 (2018) - [i5]Kijung Shin, Euiwoong Lee, Jinoh Oh, Mohammad Hammoud, Christos Faloutsos:
DiSLR: Distributed Sampling with Limited Redundancy For Triangle Counting in Graph Streams. CoRR abs/1802.04249 (2018) - 2017
- [j3]Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, Christos Faloutsos:
Graph-Based Fraud Detection in the Face of Camouflage. ACM Trans. Knowl. Discov. Data 11(4): 44:1-44:26 (2017) - [j2]Kijung Shin, Lee Sael, U Kang:
Fully Scalable Methods for Distributed Tensor Factorization. IEEE Trans. Knowl. Data Eng. 29(1): 100-113 (2017) - [c13]Kijung Shin:
WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams. ICDM 2017: 1087-1092 - [c12]Kijung Shin, Euiwoong Lee, Dhivya Eswaran, Ariel D. Procaccia:
Why You Should Charge Your Friends for Borrowing Your Stuff. IJCAI 2017: 395-401 - [c11]Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos:
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams. KDD 2017: 1057-1066 - [c10]Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, Jürgen Pfeffer:
zooRank: Ranking Suspicious Entities in Time-Evolving Tensors. ECML/PKDD (1) 2017: 68-84 - [c9]Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos:
D-Cube: Dense-Block Detection in Terabyte-Scale Tensors. WSDM 2017: 681-689 - [c8]Jinoh Oh, Kijung Shin, Evangelos E. Papalexakis, Christos Faloutsos, Hwanjo Yu:
S-HOT: Scalable High-Order Tucker Decomposition. WSDM 2017: 761-770 - [i4]Kijung Shin, Euiwoong Lee, Dhivya Eswaran, Ariel D. Procaccia:
Why You Should Charge Your Friends for Borrowing Your Stuff. CoRR abs/1705.07343 (2017) - [i3]Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos:
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams. CoRR abs/1706.03374 (2017) - [i2]Kijung Shin:
WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams. CoRR abs/1709.03147 (2017) - 2016
- [j1]Jinhong Jung, Kijung Shin, Lee Sael, U Kang:
Random Walk with Restart on Large Graphs Using Block Elimination. ACM Trans. Database Syst. 41(2): 12:1-12:43 (2016) - [c7]Kijung Shin, Tina Eliassi-Rad, Christos Faloutsos:
CoreScope: Graph Mining Using k-Core Analysis - Patterns, Anomalies and Algorithms. ICDM 2016: 469-478 - [c6]Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos:
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. KDD 2016: 895-904 - [c5]Kijung Shin, Bryan Hooi, Christos Faloutsos:
M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees. ECML/PKDD (1) 2016: 264-280 - [c4]Hemank Lamba, Vaishnavh Nagarajan, Kijung Shin, Naji Shajarisales:
Incorporating Side Information in Tensor Completion. WWW (Companion Volume) 2016: 65-66 - 2015
- [c3]Kijung Shin, Jinhong Jung, Lee Sael, U Kang:
BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs. SIGMOD Conference 2015: 1571-1585 - 2014
- [c2]Dongyeop Kang, Woosang Lim, Kijung Shin, Lee Sael, U Kang:
Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression. CIKM 2014: 1269-1278 - [c1]Kijung Shin, U Kang:
Distributed Methods for High-Dimensional and Large-Scale Tensor Factorization. ICDM 2014: 989-994 - [i1]Kijung Shin, U Kang:
Distributed Methods for High-dimensional and Large-scale Tensor Factorization. CoRR abs/1410.5209 (2014)
Coauthor Index
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