Anomaly detection in online social networks. Savage, D., Zhang, X., Yu, X., Chou, P., & Wang, Q. Social Networks, 39:62--70, October, 2014. ![Anomaly detection in online social networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{savage_anomaly_2014,
title = {Anomaly detection in online social networks},
volume = {39},
issn = {0378-8733},
url = {http://www.sciencedirect.com/science/article/pii/S0378873314000331},
doi = {10.1016/j.socnet.2014.05.002},
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.},
urldate = {2014-07-28},
journal = {Social Networks},
author = {Savage, David and Zhang, Xiuzhen and Yu, Xinghuo and Chou, Pauline and Wang, Qingmai},
month = oct,
year = {2014},
keywords = {Anomaly detection, Link analysis, Link mining, Online social networks, social network analysis},
pages = {62--70},
file = {ScienceDirect Full Text PDF:files/49538/Savage et al. - 2014 - Anomaly detection in online social networks.pdf:application/pdf;ScienceDirect Snapshot:files/49539/S0378873314000331.html:text/html}
}
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