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.

View at publisher


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.

Impact and interest:

2 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 61932
Item Type: Conference Paper
Refereed: Yes
Keywords: Anomaly detection, Online social networks, Unsupervised clustering, Graph theory
DOI: 10.1145/2480362.2480474
ISBN: 9781450316569
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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page