A semi-supervised graph-based algorithm for detecting outliers in online-social-networks
Hassanzadeh, Reza & Nayak, Richi (2013) A semi-supervised graph-based algorithm for detecting outliers in online-social-networks. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, ACM, Coimbra, Portugal, pp. 577-582.
In this paper, we propose a semi-supervised approach of anomaly detection in Online Social Networks. The social network is modeled as a graph and its features are extracted to detect anomaly. A clustering algorithm is then used to group users based on these features and fuzzy logic is applied to assign degree of anomalous behavior to the users of these clusters. Empirical analysis shows effectiveness of this method.
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|Item Type:||Conference Paper|
|Keywords:||Anomaly detection, Online social networks, Unsupervised clustering, Graph theory|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 ACM|
|Deposited On:||20 Aug 2013 23:37|
|Last Modified:||21 Aug 2013 22:48|
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