Abstract
Modeling and analyzing criminal and terrorists networks is a challenging problem that has attracted considerable attention in the academia, industry and government institutions, especially intelligence services. Criminals try to keep their communications and interactions uncovered as much as possible in order not to be discovered and resolved. Their success is our society failure and vice versa. Hence, it is essential to thoroughly study such networks to investigate their details. However, incompleteness of criminal networks is one of the main problems facing investigators, due to missing links in the network; and social network methods could be effectively used to analyze and hopefully prevent, avoid or stop criminal activities. Social network analysis can be applied to criminal networks in order to elaborate on good strategies to prosecute or prevent criminal activities. Having all this in mind, our research provides a method to identify hidden links between nodes in a network using the current information available to investigators. The method presented generates networks that represent all the possible hidden links, and the links of these generated networks represent the number of times the two entities are indirectly connected in each relationship type. The method was tested on multiple small terrorism data sets and the results demonstrate that the proposed method is capable of finding interesting hidden links. This is a valuable technique in criminal network analysis, because it can assist investigators in finding hidden links in the network and reduce the amount of missing data.
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Notes
- 1.
We will interchangeably use criminal and terrorist networks, though the two are different and have different targets, but the same analysis applies to both networks.
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Chen, A., Gao, S., Karampelas, P., Alhajj, R., Rokne, J. (2011). Finding Hidden Links in Terrorist Networks by Checking Indirect Links of Different Sub-Networks. In: Wiil, U.K. (eds) Counterterrorism and Open Source Intelligence. Lecture Notes in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0388-3_8
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DOI: https://doi.org/10.1007/978-3-7091-0388-3_8
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