Joint community and anomaly tracking in dynamic networks
This addresses the problem of improving reliability in community detection for applications like targeted advertising and fraud prevention in dynamic networks, though it appears incremental as it builds on existing dynamic factor models.
The paper tackled the problem of unreliable community assignments in dynamic networks due to anomalous user behavior by proposing a novel approach for jointly tracking communities and detecting anomalous nodes. The result was successful unveiling of underlying communities and anomalous nodes in experiments on synthetic and real network time series.
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. Efficient tracking algorithms suitable for both online and decentralized operation are developed. Experiments conducted on both synthetic and real network time series successfully unveil underlying communities and anomalous nodes.