Integrating relations and criminal background to identifying key individuals in crime networks

Abstract One of the most common methods used in the social network analysis of criminal groups is node importance evaluation, which focuses on the links between network members to identify likely crime suspects. Because such traditional node evaluators do not take full advantage of group members' individual criminal propensities, a new evaluator called the social network criminal suspect evaluator (SNCSE) is proposed. SNCSE incorporates members' individual criminal propensities into the node importance evaluation and employs a novel perspective based on concepts of human and social capital, an ego network structure, and an analogy between social interaction and field theory. SNCSE is applied to solve two real-world problems. Its effectiveness is compared with that of traditional evaluators. The results show that integrating criminal propensity into network analysis enables the more accurate identification of key suspects compared to alternative evaluators.

[1]  Fredy Troncoso,et al.  A novel approach to detect associations in criminal networks , 2020, Decis. Support Syst..

[2]  Rosanna Grassi,et al.  Betweenness to assess leaders in criminal networks: New evidence using the dual projection approach , 2019, Soc. Networks.

[3]  Mahdi Jalili,et al.  Finding influential nodes in social networks based on neighborhood correlation coefficient , 2020, Knowl. Based Syst..

[4]  A. Dnes,et al.  Behavior, Human Capital and the Formation of Gangs , 2010 .

[5]  Fredy Humberto Troncoso Espinosa,et al.  Prediction of Recidivism in Thefts and Burglaries Using Machine Learning , 2020 .

[6]  Hsinchun Chen,et al.  Analyzing and Visualizing Criminal Network Dynamics: A Case Study , 2004, ISI.

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

[8]  Wassily Leontief.  Input-Output Economics , 1966 .

[9]  Lv Le,et al.  A New Method for Evaluating Node Importance in Complex Networks Based on Data Field Theory , 2010, 2010 First International Conference on Networking and Distributed Computing.

[10]  He Nan,et al.  Evaluate Nodes Importance in the Network Using Data Field Theory , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[11]  Lise Getoor,et al.  Link mining: a survey , 2005, SKDD.

[12]  Ee-Peng Lim,et al.  Social Network Discovery by Mining Spatio-Temporal Events , 2005, Comput. Math. Organ. Theory.

[13]  Wang Jiaxin,et al.  Investigative Data Mining: Identifying Key Nodes in Terrorist Networks , 2006, 2006 IEEE International Multitopic Conference.

[14]  Hsinchun Chen,et al.  Analyzing Terrorist Networks: A Case Study of the Global Salafi Jihad Network , 2005, ISI.

[15]  Jie Wu,et al.  Evaluate Nodes Importance in Directed Network Using Topological Potential , 2010, 2010 2nd International Conference on Information Engineering and Computer Science.

[16]  Jeffrey Scott McIllwain,et al.  Organized crime: A social network approach , 1999 .

[17]  Bart Baesens,et al.  Predicting tax avoidance by means of social network analytics , 2018, Decis. Support Syst..

[18]  David Kirk,et al.  Social Network Analysis , 2010 .

[19]  R. Hulst.  Introduction to Social Network Analysis (SNA) as an investigative tool , 2009 .

[20]  Zhong Liu,et al.  Evaluation method of effect from network attack considering node multi-property feature , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[21]  Monique Snoeck,et al.  APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions , 2015, Decis. Support Syst..

[22]  R. Keast,et al.  Social network analysis of terrorist networks: can it add value? , 2012 .

[23]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[24]  Hsinchun Chen,et al.  Using Coplink to Analyze Criminal-Justice Data , 2002, Computer.

[25]  Lester C. Thurow,et al.  INVESTMENT IN HUMAN CAPITAL , 2016 .

[26]  J. Coleman,et al.  Social Capital in the Creation of Human Capital , 1988, American Journal of Sociology.

[27]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[28]  C. J. Rhodes,et al.  Social network topology: a Bayesian approach , 2007, J. Oper. Res. Soc..

[29]  Malcolm K. Sparrow,et al.  The application of network analysis to criminal intelligence: An assessment of the prospects , 1991 .

[30]  Robert M. Solow,et al.  On the Structure of Linear Models , 1952 .

[31]  Katherine Faust,et al.  Social Networks and Crime: Pitfalls and Promises for Advancing the Field , 2019, Annual Review of Criminology.

[32]  P. Santhi Thilagam,et al.  Discovering suspicious behavior in multilayer social networks , 2017, Comput. Hum. Behav..