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• W2038841853 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 networks. By postulating edge creation as the result of mutual community participation by node pairs, a dynamic factor model with anomalous memberships captured through a sparse outlier matrix is put forth. Formulated as a time-varying, outlier-aware, non-negative matrix factorization problem, an efficient tracking algorithm is developed. The efficacy of the proposed approach is demonstrated on synthetic network time series generated using the stochastic block model." @default.
• W2038841853 created "2016-06-24" @default.
• W2038841853 creator A5049332649 @default.
• W2038841853 creator A5067879588 @default.
• W2038841853 date "2014-12-01" @default.
• W2038841853 modified "2023-09-26" @default.
• W2038841853 title "Tracking anomalous community memberships in time-varying networks" @default.
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• W2038841853 doi "https://doi.org/10.1109/globalsip.2014.7032243" @default.
• W2038841853 hasPublicationYear "2014" @default.
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