Notice of Violation of IEEE Publication PrinciplesNovel Algorithms for Subgroup Detection in Terrorist Networks

Discovery of the organizational structure of terrorist networks leads investigators to terrorist cells. Therefore, detection of covert networks from terrorists' data is important to terrorism investigation and prevention of future terrorist activity. In this paper, we discuss this important area of subgroup detection in terrorist networks, propose novel algorithms for subgroup detection, and present a demonstration system that we have implemented.

[1]  Hsinchun Chen,et al.  CrimeNet explorer: a framework for criminal network knowledge discovery , 2005, TOIS.

[2]  A. John.  MINING GRAPH DATA , 2022 .

[3]  Hsinchun Chen,et al.  COPLINK Center: Information and Knowledge Management for Law Enforcement , 2004, DG.O.

[4]  Eytan Adar,et al.  GUESS: a language and interface for graph exploration , 2006, CHI.

[5]  Hsinchun Chen,et al.  Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks , 2004, Decis. Support Syst..

[6]  Nasrullah Memon,et al.  Practical Algorithms for Destabilizing Terrorist Networks , 2006, ISI.

[7]  Ulrik Brandes,et al.  Analysis and Visualization of Social Networks , 2003, Graph Drawing Software.

[8]  Jesus Mena,et al.  Investigative Data Mining for Security and Criminal Detection , 2002 .

[9]  Gang Wang,et al.  Crime data mining: a general framework and some examples , 2004, Computer.

[10]  Lise Getoor,et al.  Link mining: a new data mining challenge , 2003, SKDD.

[11]  Catherine Plaisant,et al.  TreePlus: Interactive Exploration of Networks with Enhanced Tree Layouts , 2006, IEEE Transactions on Visualization and Computer Graphics.

[12]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[13]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[14]  David L. Hicks,et al.  Extracting Information from Semi-structured Web Documents: A Framework , 2008, APWeb Workshops.

[15]  Padhraic Smyth,et al.  Analysis and Visualization of Network Data using JUNG , 2005 .

[16]  David L. Hicks,et al.  How Investigative Data Mining Can Help Intelligence Agencies to Discover Dependence of Nodes in Terrorist Networks , 2007, ADMA.

[17]  Vladimir Batagelj,et al.  Exploratory Social Network Analysis with Pajek , 2005 .

[18]  Nasrullah Memon.  Investigative Data Mining: Mathematical Models for Analyzing, Visualizing and Destabilizing Terrorist Networks , 2007 .

[19]  John Scott.  Social Network Analysis , 1988 .

[20]  B. Wellman.  The Development of Social Network Analysis: A Study in the Sociology of Science , 2008 .

[21]  H.L. Larsen,et al.  Notice of Violation of IEEE Publication PrinciplesHarvesting Terrorists Information from Web , 2007, 2007 11th International Conference Information Visualization (IV '07).

[22]  Duncan J. Watts,et al.  Six Degrees: The Science of a Connected Age , 2003 .

[23]  Brian N. Smith,et al.  Social Network Analysis , 2007 .

[24]  Catherine Plaisant,et al.  NetLens: Iterative Exploration of Content-Actor Network Data , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.

[25]  Miryung Kim,et al.  SoftGUESS: Visualization and Exploration of Code Clones in Context , 2007, 29th International Conference on Software Engineering (ICSE'07).

[26]  Ted E. Senator,et al.  Link mining applications: progress and challenges , 2005, SKDD.

[27]  John Scott.  What is social network analysis , 2010 .

[28]  Stéphane Bressan,et al.  Extracting Information from Semi-structured Web Documents , 2002, OOIS Workshops.

[29]  Lawrence B. Holder,et al.  Graph-Based Data Mining , 2000, IEEE Intell. Syst..

[30]  David L. Hicks,et al.  Understanding the structure of terrorist networks , 2007, Int. J. Bus. Intell. Data Min..