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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 308675, 10 pages
Research Article

Crime Busting Model Based on Dynamic Ranking Algorithms

1College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2College of Overseas Education, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3School of Engineering and Computing Sciences, New York Institute of Technology, Old Westbury, NY 11568-8000, USA

Received 28 May 2013; Accepted 11 June 2013

Academic Editor: Xinsong Yang

Copyright © 2013 Yang Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposed a crime busting model with two dynamic ranking algorithms to detect the likelihood of a suspect and the possibility of a leader in a complex social network. Signally, in order to obtain the priority list of suspects, an advanced network mining approach with a dynamic cumulative nominating algorithm is adopted to rapidly reduce computational expensiveness than most other topology-based approaches. Our method can also greatly increase the accuracy of solution with the enhancement of semantic learning filtering at the same time. Moreover, another dynamic algorithm of node contraction is also presented to help identify the leader among conspirators. Test results are given to verify the theoretical results, which show the great performance for either small or large datasets.