NDLI: Anomaly detection in Online Social Networks using structure-based technique

Content Provider	IEEE Xplore Digital Library
Author	Rezaei, Abdolazim Kasirun, Zarinah Mohd Rohani, Vala Ali Khodadadi, Touraj
Copyright Year	2013
Description	Author affiliation: Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia (Khodadadi, Touraj) \|\| Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia (Rezaei, Abdolazim; Kasirun, Zarinah Mohd; Rohani, Vala Ali)
Abstract	Online Social Networks as new phenomenon have affected our life in many positive ways; however it can be considered as way of malicious activities. Identifying anomalous users has become a challenge and many researches are conducted but they are not enough and in this paper we propose a methodology based on graph metrics of online social networks. The experimental results illustrate that majority of friends in online social networks have common friends with their friends while anomalous users may not follow this fact.
Starting Page	619
Ending Page	622
File Size	667163
Page Count	4
File Format	PDF
ISBN	9781908320209
DOI	10.1109/ICITST.2013.6750277
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-09
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Infonomics Society
Subject Keyword	Atmospheric measurements Image edge detection Particle measurements Robustness Power capacitors Programming profession Anomaly detection Graph mining Online social networks
Content Type	Text
Resource Type	Article

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