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10 May 2006 Identifying and tracking dynamic processes in social networks
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Abstract
The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream— the Enron email corpus.
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Wayne Chung, Robert Savell, Jan-Peter Schütt, and George Cybenko "Identifying and tracking dynamic processes in social networks", Proc. SPIE 6201, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense V, 620105 (10 May 2006); https://doi.org/10.1117/12.670127
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