Research

Analyzing covert social network foundation behind terrorism disaster
A new method is presented, which aids a terrorism investigator in analyzing the covert social network foundation hidden behind the terrorism disaster. It is to solve a node discovery problem, which means to discover a node, which functions relevantly in a social network, but escaped from monitoring on the presence and mutual relationship of nodes. The method aims at integrating the expert investigator's prior understanding, insight on the terrorists' social network nature derived from the complex graph theory, and computational data processing. The social network responsible for the 9/11 attack in 2001 is used to execute simulation experiment to evaluate the performance of the method.

Discovering covert node in networked organization
A new method is presented, which aids a homeland security expert in solving a node discovery problem in a network. Covert nodes which exist in a social network do not appear in the records which are observed on the communication or collaborative activities among the nodes. Discovering the covert nodes refers to identifying suspicious records in which the covert nodes would appear, or suspicious nodes which would be the neighbors of the covert nodes, if the covert nodes became overt. Based on probability theory, a mathematical model is developed for the maximal likelihood estimation of the network and for the identification of the suspicious records. Precision, recall, and F measure characteristics are demonstrated with the test dataset generated from a real organization and computational synthesis. The performance is close to the theoretical limit for any target covert nodes, network topologies, and network sizes if the ratio of the number of the observed data to the number of the possible communication patterns is high enough.

Profiling of a network behind an infectious disease outbreak
A new method is presented, which aids an infectious disease controller in estimating a social network topology and diffusion parameters from the time sequence data of an infectious disease outbreak. The method is applicable to a stochastic diffusion process in a meta-population and SIR (Susceptible, Infectious, and Recovered) model over a social network. The method is based on the maximal likelihood estimation from the data on the number of the infectious persons. I demonstrate the performance of the method of profiling with the WHO (World Health Organization) report on SARS (Severe Acute Respiratory Syndrome) pandemic in 2003.

Reflective visualization and verbalization of unconscious preference
A new method is presented, which aids a marketing practitioner in helping a person become aware of his or her unconscious preferences, and convey them to others in the form of verbal explanation. The method combines the concepts of reflection, visualization, and verbalization. The method was tested in an experiment where the unconscious preferences of the subjects for various artworks were investigated. In the experiment, two lessons were learned. The first is that it helps the subjects become aware of their unconscious preferences to verbalize weak preferences as compared with strong preferences through discussion over preference diagrams. The second is that it is effective to introduce an adjustable factor into visualization to adapt to the differences in the subjects and to foster their mutual understanding.

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