NDLI: Detecting Anomalies in Time-Varying Networks Using Tensor Decomposition

Content Provider	IEEE Xplore Digital Library
Author	Sapienza, A. Panisson, A. Wu, J. Gauvin, L. Cattuto, C.
Copyright Year	2015
Description	Author affiliation: Sch. of Public Health, Univ. of Hong Kong, Hong Kong, China (Wu, J.) \|\| Polytech. Univ. of Turin, Turin, Italy (Sapienza, A.) \|\| Data Sci. Lab., ISI Found., Turin, Italy (Panisson, A.; Gauvin, L.; Cattuto, C.)
Abstract	New 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 high-resolution 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.
Starting Page	516
Ending Page	523
File Size	998520
Page Count	8
File Format	PDF
ISSN	23759259
e-ISBN	9781467384933
DOI	10.1109/ICDMW.2015.128
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Conferences Time series analysis Cleaning Time measurement Iterative methods Data mining
Content Type	Text
Resource Type	Article

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