Anomaly detection in online social networks

Savage, D, Zhang, X, Yu, X, Chou, P and Wang, Q 2014, 'Anomaly detection in online social networks', Social Networks, vol. 39, no. 1, pp. 62-70.


Document type: Journal Article
Collection: Journal Articles

Title Anomaly detection in online social networks
Author(s) Savage, D
Zhang, X
Yu, X
Chou, P
Wang, Q
Year 2014
Journal name Social Networks
Volume number 39
Issue number 1
Start page 62
End page 70
Total pages 9
Publisher Elsevier BV
Abstract Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being labelled or unlabelled, and survey methods for detecting these different types of anomalies. We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space. In addition, this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas for future research.
Subject Pattern Recognition and Data Mining
Keyword(s) Anomaly detection
Link analysis
Link mining
Online social networks
Social network analysis
Copyright notice © 2014 Elsevier B.V. All rights reserved.
ISSN 0378-8733
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