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A novel approach to detect associations in criminal networks
یک رویکرد جدید برای شناسایی انجمن ها در شبکه های جنایی-2020 Understanding criminal groups as social networks has led to the design of powerful systems for decision support
in criminal investigative work. Tools using the methods of social network analysis have proven particularly
effective in the identification of associations between individuals whose relationships are not otherwise evident.
This identification is typically based on the links between individuals and does not account for other relevant
information, such as individual attributes. The present study proposes a new model for identifying criminal
associations that incorporates this type of data. Built around a linear association model, this approach identifies
the principal association between two individuals. Assuming one of the individuals as the crime planner, the
approach can be used to maximize his/her utility function. The model is compared with an existing algorithm for
identifying associations using a real dataset provided by the Public Prosecutors Office of Región del Biobío-
Chile. The results demonstrate the proposed models effectiveness and flexibility in generating different association
alternatives, a particularly useful feature that contributes to the more efficient use of criminal investigation
resources. Keywords: Crime analytics | Social networks | Association | Rational choice | Criminal propensity |
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