Comparison of Feature-Based Criminal Network Detection Models with k-Core and n-Clique

Author

Ozgul, Fatih ; Erdem, Zeki ; Bowerman, Chris ; Atzenbeck, Claus

Author_Institution

Dept. of Comput., Univ. of Sunderland, Sunderland, UK

fYear

2010

fDate

9-11 Aug. 2010

Firstpage

400

Lastpage

401

Abstract

Four group detection models (e.g. GDM, OGDM, SoDM and ComDM) are developed based on crime data features. These detection models are compared more common baseline SNA group detection algorithms. It is intended to find out, whether these four crime data specific group detection models can perform better than widely used k-core and n-clique algorithms. Two data sets which contain previously known criminal networks are used as testbeds.

Keywords

graph theory; police data processing; social networking (online); SNA group detection algorithm; crime data features; crime data specific group detection model; feature based criminal network detection model; group detection model; k-core; n-clique; social network analysis; Computational modeling; Data mining; Data models; Drugs; Feature extraction; Social network services; Terrorism; criminal networks; detection; k-core; n-clique;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on

Conference_Location

Odense

Print_ISBN

978-1-4244-7787-6

Electronic_ISBN

978-0-7695-4138-9

Type

conf

DOI

10.1109/ASONAM.2010.45

Filename

5563073

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1918524