Anomaly detection in online social networks

被引：173

作者：

Savage, David ^{[1
]}

Zhang, Xiuzhen ^{[1
]}

Yu, Xinghuo ^{[1
]}

Chou, Pauline ^{[1
,2
]}

Wang, Qingmai ^{[1
]}

机构：

[1] RMIT Univ, Sch CS&IT, Melbourne, Vic 3001, Australia

[2] Australian Transact Reports & Anal Ctr, Melbourne, Vic 8010, Australia

来源：

SOCIAL NETWORKS | 2014年 / 39卷

基金：

澳大利亚研究理事会;

关键词：

Anomaly detection; Link mining; Link analysis; Social network analysis; Online social networks; NOVELTY DETECTION; WEB;

D O I：

10.1016/j.socnet.2014.05.002

中图分类号：

Q98 [人类学];

学科分类号：

030303 ;

摘要：

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. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：62 / 70

页数：9

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