Article

M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees

Authors:

Christos FaloutsosAuthors Info & Claims

ECML PKDD 2016: European Conference on Machine Learning and Knowledge Discovery in Databases - Volume 9851

Pages 264 - 280

https://doi.org/10.1007/978-3-319-46128-1_17

Published: 19 September 2016 Publication History

Abstract

Given a large-scale and high-order tensor, how can we find dense blocks in it__ __ Can we find them in near-linear time but with a quality guarantee__ __ Extensive previous work has shown that dense blocks in tensors as well as graphs indicate anomalous or fraudulent behavior e.g., lockstep behavior in social networks. However, available methods for detecting such dense blocks are not satisfactory in terms of speed, accuracy, or flexibility. In this work, we propose M-Zoom, a flexible framework for finding dense blocks in tensors, which works with a broad class of density measures. M-Zoom has the following properties: 1 Scalable: M-Zoom scales linearly with all aspects of tensors and is upï źto 114$$\times $$faster than state-of-the-art methods with similar accuracy. 2 Provably accurate: M-Zoom provides a guarantee on the lowest density of the blocks it finds. 3 Flexible: M-Zoom supports multi-block detection and size bounds as well as diverse density measures. 4 Effective: M-Zoom successfully detected edit wars and bot activities in Wikipedia, and spotted network attacks from a TCP dump with near-perfect accuracy AUCï ź=ï ź0.98. The data and software related to this paper are available at http://www.cs.cmu.edu/~kijungs/codes/mzoom/.

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Cited By

Feng WLiu SCheng X(2023)Hierarchical Dense Pattern Detection in TensorsACM Transactions on Knowledge Discovery from Data10.1145/357702217:6(1-29)Online publication date: 28-Feb-2023
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Li ZSong JHu ZWang ZGao J(2021)Constrained Dual-Level Bandit for Personalized Impression Regulation in Online Ranking SystemsACM Transactions on Knowledge Discovery from Data10.1145/346134016:2(1-23)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3461340
Wang LLi PXiong KZhao JLin RDemartini GZuccon GCulpepper JHuang ZTong H(2021)Modeling Heterogeneous Graph Network on Fraud DetectionProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482277(1959-1968)Online publication date: 26-Oct-2021
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Published In

cover image Guide Proceedings

ECML PKDD 2016: European Conference on Machine Learning and Knowledge Discovery in Databases - Volume 9851

September 2016

811 pages

ISBN:9783319461274

Editors:
Paolo Frasconi
Universití degli Studi di Firenze, Firenze, Italy
,
Niels Landwehr
Computer Science, University of Potsdam, Potsdam, Germany
,
Giuseppe Manco
High Performance Computing and Networks, Rende, Italy
,
Jilles Vreeken
MPI for Informatics, Saarland University, Saarbrücken, Germany

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 19 September 2016

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Cited By

Feng WLiu SCheng X(2023)Hierarchical Dense Pattern Detection in TensorsACM Transactions on Knowledge Discovery from Data10.1145/357702217:6(1-29)Online publication date: 28-Feb-2023
https://dl.acm.org/doi/10.1145/3577022
Li ZSong JHu ZWang ZGao J(2021)Constrained Dual-Level Bandit for Personalized Impression Regulation in Online Ranking SystemsACM Transactions on Knowledge Discovery from Data10.1145/346134016:2(1-23)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3461340
Wang LLi PXiong KZhao JLin RDemartini GZuccon GCulpepper JHuang ZTong H(2021)Modeling Heterogeneous Graph Network on Fraud DetectionProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482277(1959-1968)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482277
Duong QRamampiaro HNørvåg KDam T(2021)Density Guarantee on Finding Multiple Subgraphs and SubtensorsACM Transactions on Knowledge Discovery from Data10.1145/344666815:5(1-32)Online publication date: 10-May-2021
https://dl.acm.org/doi/10.1145/3446668
Sun XZhang JZhao QLiu SChen JZhuang RShen HCheng X(2021)CubeFlow: Money Laundering Detection with Coupled TensorsAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-75762-5_7(78-90)Online publication date: 11-May-2021
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Zhang HZheng WChen CGao KHu YHuang LXu W(2020)Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative FrameworkProceedings of The Web Conference 202010.1145/3366423.3380213(1389-1399)Online publication date: 20-Apr-2020
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Zhao TDeng CYu KJiang TWang DJiang Md'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Error-Bounded Graph Anomaly Loss for GNNsProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411979(1873-1882)Online publication date: 19-Oct-2020
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Li ZJin XZhuang CSun Z(2020)Content-Aware Anomaly Detection with Network Representation LearningAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-60248-2_4(50-64)Online publication date: 2-Oct-2020
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Zheng LLi ZLi JLi ZGao J(2019)AddgraphProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367656(4419-4425)Online publication date: 10-Aug-2019
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Yikun BXin LLing HYitao DXue LWei X(2019)No Place to Hide: Catching Fraudulent Entities in TensorsThe World Wide Web Conference10.1145/3308558.3313403(83-93)Online publication date: 13-May-2019
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