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Conference Paper: Detecting anomalies in time-varying networks using tensor decomposition

TitleDetecting anomalies in time-varying networks using tensor decomposition
Authors
Issue Date2015
PublisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001620
Citation
Proceedings of the15th IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA, 14-17 November 2015, p. 516-523 How to Cite?
AbstractNew data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as time-varying networks. This brings forth new challenges for the identification and removal of time-varying graph anomalies that entail complex correlations of topological features and temporal activity patterns. Here we present an anomaly detection approach for temporal graph data, based on an iterative tensor decomposition and masking procedure. We test this approach using highresolution social network data from wearable proximity sensors. The dataset includes metadata that allow to independently build a ground truth, used to validate the anomaly detection method. Our approach achieves high accuracy in identifying meso-scale network anomalies due to sensor wearing protocol, proving the practical viability of the method for a real-world application.
Persistent Identifierhttp://hdl.handle.net/10722/247121

 

DC FieldValueLanguage
dc.contributor.authorSapienza, A-
dc.contributor.authorPanisson, A-
dc.contributor.authorWu, JTK-
dc.contributor.authorGauvin, L-
dc.contributor.authorCattuto, C-
dc.date.accessioned2017-10-18T08:22:37Z-
dc.date.available2017-10-18T08:22:37Z-
dc.date.issued2015-
dc.identifier.citationProceedings of the15th IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA, 14-17 November 2015, p. 516-523-
dc.identifier.urihttp://hdl.handle.net/10722/247121-
dc.description.abstractNew data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as time-varying networks. This brings forth new challenges for the identification and removal of time-varying graph anomalies that entail complex correlations of topological features and temporal activity patterns. Here we present an anomaly detection approach for temporal graph data, based on an iterative tensor decomposition and masking procedure. We test this approach using highresolution social network data from wearable proximity sensors. The dataset includes metadata that allow to independently build a ground truth, used to validate the anomaly detection method. Our approach achieves high accuracy in identifying meso-scale network anomalies due to sensor wearing protocol, proving the practical viability of the method for a real-world application.-
dc.languageeng-
dc.publisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001620-
dc.relation.ispartofIEEE International Conference on Data Mining Workshop-
dc.rightsIEEE International Conference on Data Mining Workshop. Copyright © IEEE, Computer Society.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleDetecting anomalies in time-varying networks using tensor decomposition-
dc.typeConference_Paper-
dc.identifier.emailWu, JTK: joewu@hku.hk-
dc.identifier.authorityWu, JTK=rp00517-
dc.identifier.doi10.1109/ICDMW.2015.128-
dc.identifier.hkuros280786-
dc.identifier.spage516-
dc.identifier.epage523-
dc.publisher.placeUnited States-

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