A Probabilistic computational model for identifying organizational structures from uncertain message data

The knowledge of the principles and goals under which an adversary organization operates is required to predict its future activities. To implement successful counter-actions, additional knowledge of the specifics of the organizational structures, such as command, communication, control, and information access networks, as well as responsibility distribution among members of the organization, is required. In this paper, we employ a Hidden Markov Random Field (HMRF) model and a graph matching algorithm to discover the attributes of and relationships among organizational members, assets, environment areas, and mission tasks. We focus on identifying the mapping between hypothesized nodes of enemy command organization and tracked individuals and resources. This also allows us to compute the posterior energy function quantifying the belief that the observed data has been generated by a particular organization. The experiment results show that our probabilistic model and the Simulated Annealing search algorithm can accurately identify the different organizational structures and achieve correct node mappings among organizational members.