Visible to the public Detecting anomalies in Online Social Networks using graph metrics

TitleDetecting anomalies in Online Social Networks using graph metrics
Publication TypeConference Paper
Year of Publication2015
AuthorsKaur, R., Singh, S.
Conference Name2015 Annual IEEE India Conference (INDICON)
KeywordsAnomaly, anomaly activity detection, Brokerage, Cliques, Curve fitting, feature extraction, Fitting, graph metrics, graph theory, Image edge detection, Measurement, Oddball algorithm, online social networks, personalized user profile, pubcrawl170111, regression, security of data, Social network services, social networking (online), Stars, structural characteristic analysis
Abstract

Online Social Networks have emerged as an interesting area for analysis where each user having a personalized user profile interact and share information with each other. Apart from analyzing the structural characteristics, detection of abnormal and anomalous activities in social networks has become need of the hour. These anomalous activities represent the rare and mischievous activities that take place in the network. Graphical structure of social networks has encouraged the researchers to use various graph metrics to detect the anomalous activities. One such measure that seemed to be highly beneficial to detect the anomalies was brokerage value which helped to detect the anomalies with high accuracy. Also, further application of the measure to different datasets verified the fact that the anomalous behavior detected by the proposed measure was efficient as compared to the already proposed measures in Oddball Algorithm.

URLhttps://ieeexplore.ieee.org/document/7443800
DOI10.1109/INDICON.2015.7443800
Citation Keykaur_detecting_2015