Locating hidden groups in communication networks using hidden Markov models
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Understanding the structure of terrorist networks
International Journal of Business Intelligence and Data Mining
Computerized Simulation in the Social Sciences
Simulation and Gaming
Data-to-model: a mixed initiative approach for rapid ethnographic assessment
Computational & Mathematical Organization Theory
Social-Psychological harmonic oscillators in the self-regulation of organizations and systems
QI'12 Proceedings of the 6th international conference on Quantum Interaction
Evolutionary Community Detection for Observing Covert Political Elite Cliques
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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Covert networks are often difficult to reason about, manage and destabilize. In part, this is because they are a complex adaptive system. In addition, this is due to the nature of the data available on these systems. Making these covert networks less adaptive, more predictable, more consistent will make it easier to contain or constrain their activity. But, how can we inhibit adaptation? Herein, covert networks are characterized as dynamic multi-mode multi-plex networks. Dynamic network analysis tools are used to assess their structure and identify effective destabilization strategies that inhibit the adaptivity of these groups.