Name
Abstract | ||
|---|---|---|
In this paper, we analyze co-offending networks derived from a large real-world crime dataset for the purpose of identifying organized crime structures and their constituent entities. We focus on methodical and analytical aspects in using social network analysis methods and data mining techniques. The goal of our work is to promote computational co-offending network analysis as an effective means for extracting information about criminal organizations from large real-life crime datasets, specifically police-reported crime data. We contend that it would be virtually impossible to obtain such information by using traditional crime analysis methods. For our approach we provide an experimental evaluation with promising results. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ASONAM.2012.96 | Advances in Social Networks Analysis and Mining |
Keywords | Field | DocType |
analytical aspect,police-reported crime data,organized crime structure,computational co-offending network analysis,social network analysis perspective,organized crime groups,constituent entity,social network analysis method,traditional crime analysis method,large real-world crime dataset,large real-life crime datasets,data mining technique,data mining,social sciences | Organizational network analysis,Data mining,Crime data,Computer science,Social network analysis,Network analysis,Organised crime,Crime analysis | Conference |
ISBN | Citations | PageRank |
978-1-4673-2497-7 | 2 | 0.46 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad A. Tayebi | 1 | 50 | 7.59 |
| Uwe Glasser | 2 | 17 | 2.00 |