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2016

Joint Community and Anomaly Tracking in Dynamic Networks

3 years 7 months ago
Joint Community and Anomaly Tracking in Dynamic Networks
Abstract—Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection finds many practical applications in diverse settings. Communities in online social networks are indicative of shared functional roles, or affiliation to a common socio-economic status, the knowledge of which is vital for targeted advertisement. In buyer-seller networks, community detection facilitates better product recommendations. Unfortunately, reliability of community assignments is hindered by anomalous user behavior often observed as unfair self-promotion, or “fake” highly-connected accounts created to promote fraud. The present paper advocates a novel approach for jointly tracking communities while detecting such anomalous nodes in time-varying netwo...
Brian Baingana, Georgios B. Giannakis
Added 11 Apr 2016
Updated 11 Apr 2016
Type Journal
Year 2016
Where TSP
Authors Brian Baingana, Georgios B. Giannakis
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