Title :
Locating Central Actors in Co-offending Networks
Author :
Tayebi, Mohammad A. ; Bakker, Laurens ; Glässer, Uwe ; Dabbaghian, Vahid
Author_Institution :
Sch. of Comput. Sci., Simon Fraser Univ., Burnaby, BC, Canada
Abstract :
A co-offending network is a network of offenders who have committed crimes together. Recently different researches have shown that there is a fairly strong concept of network among offenders. Analyzing these networks can help law enforcement agencies in designing more effective strategies for crime prevention and reduction. One of the important tasks in co-offending network analysis is central actors identification. In this paper, firstly we introduce a data model, called unified crime data model to bridge the conceptual gap between abstract crime data level and co-offending network mining level. Using this data model, we extract the co-offending network of five years real-world crime data. Then we apply different variations of centrality methods on the extracted network and discuss how key player identification and removal can help law enforcement agencies in policy making for crime reduction.
Keywords :
computer crime; criminal law; data mining; mobile computing; central actors identification; central actors locating; co-offending network analysis; co-offending network mining level; crime prevention; law enforcement agencies; Analytical models; Data mining; Data models; Knowledge engineering; Law enforcement; Probabilistic logic; Social network services;
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-61284-758-0
Electronic_ISBN :
978-0-7695-4375-8
DOI :
10.1109/ASONAM.2011.120