A Prioritization Algorithm for Crime Busting based on Centrality Analysis

Detecting conspirators, which often relates to organized crimes, represents a major problem for many investigation bureaus. A prioritization algorithm based on centrality analysis was introduced. The correlation between suspects was modeled as a social network, and the degree, betweeness and eigenvector centralities were utilized to quantify the suspicion degree of individual conspirators. Due to the analysis, conspirators and non-conspirators were able to be sorted into high-suspected, low-suspected, low-unsuspected and high-unsuspected sections based on their likelihood of involving the conspiracy. A detailed scenario is studied and the efficacy of the given method is verified at the end of this paper.