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Investigative Data Mining: Mathematical Models for Analyzing, Visualizing and Destabilizing Terrorist Networks

von Nasrullah Memon

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[1.] Nm/Fragment 086 01 - Diskussion
Zuletzt bearbeitet: 2012-04-20 22:10:42 WiseWoman
Fragment, Gesichtet, Nm, SMWFragment, Schutzlevel sysop, Verschleierung, Xu etal 2004

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[Structural changes are assumed to result from some] stochastic processes of network effects such as reciprocity, transitivity, and balance (Snijders, T.A.B., 2001). In statistical analysis, links are modeled as random variables that can be in different states at different times. The purpose is to identify which network models fits best with empirical data and observed structural changes.

Discrete or continuous Markov models are often used in statistical analysis. The Markov models are based on the assumptions that a particular state of a process is dependent current state but not on any previous state (Leenders, R., 1997; Snijders, T.A.B., 1997; Snijders, T.A.B., 2001). The analysis is carried out with the help of transition and intensity matrix. Transition matrix contains the conditional probabilities one state to previous state, while the intensity matrix contains the transition rates (Hallinan, M.T.,1978/79); Leenders, R., 1997).

With statistical methods, structural changes are assumed to result from some stochastic processes of network effects such as reciprocity, transitivity, and balance [EN 34]. In this type of analysis, links are modeled as random variables that can be in different states [...] at different time. The

[P. 7]

purpose is to examine which network effect fits the empirical data and better accounts for the observed structural changes.

Discrete or continuous Markov models are often used in statistical analysis. The most important property of a Markov model is that the future state of a process is dependent only on the current state but not on any previous state [EN 25, EN 33, EN 34]. The process is governed by a transition matrix, which contains the conditional probabilities of changing from the initial state to the current state, and the intensity matrix whose elements are transition rates [EN 19, EN 25].


[EN 19] Hallinan, M.T. (1978/79). The process of friendship formation. Social Networks, 1, 193-210.

[EN 25] Leenders, R. (1997). Evolution of friendship and best friendship choices, in Evolution of social networks, P. Doreian & F.N. Stokman (eds.). Gordon and Breach: Australia.

[EN 33] Snijders, T.A.B. (1997). Stochastic actor-oriented models for network change, in Evolution of social networks, P. Doreian & F.N. Stokman (eds.). Gordon and Breach: Australia.

[EN 34] Snijders, T.A.B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31, 361-395.

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[2.] Nm/Fragment 086 25 - Diskussion
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2.13.3 Simulation Methods

The simulation methods exploit multi agent technology to analyse the network dynamics, in contrary to descriptive or statistical methods which examine social network dynamics quantitatively. In the simulation method, members in a social network are often modeled intelligent agents with ability to behave and make decisions based on certain criteria in a particular situation. The collective behaviors of all members in a network will determine [how the network evolves from one structure to another in a considered scenario.]

3.1.3 Simulation Methods

Unlike descriptive or statistical methods, which examine social network dynamics quantitatively, simulation methods rely on multi-agent technology to analyze network dynamics. In this method, members in a social network are often modeled and implemented as computer agents who have the abilities to behave and make decisions based on certain criteria. The collective behaviors of all members in a network will determine how the network evolves from one structure to another.

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