Agents Shaping Networks Shaping Agents: Integrating Social Network Analysis and Agent-Based Modeling in Computational Crime Research

  • Nicola LettieriEmail author
  • Antonio Altamura
  • Delfina Malandrino
  • Valentina Punzo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10423)


The paper presents a recent development of an interdisciplinary research exploring innovative computational approaches to the scientific study of criminal behavior. The attention is focused on an attempt to combine social network analysis and agent-based modelling into CrimeMiner, an experimental framework that seamlessly integrates document-enhancement, visualization and network analysis techniques to support the study of criminal organizations. Our goal is both methodological and scientific. We are exploring how the synergy between ABM and SNA can support a deeper and more empirically grounded understanding of the complex dynamics taking place within criminal organizations between the individual/behavioral and social/structural level.


Agent-based modeling Social network analysis Computational crime analysis 


  1. 1.
    Akers, R.L.: Deviant behavior: a social learning approach (Wadsworth, Belmont, 1977). An upper level text written from a cultural transmission perspective. Evaluates major theories of deviance and examines a wide variety of deviant activities (1973)Google Scholar
  2. 2.
    Balke, T., Cranefield, S., Di Tosto, G., Mahmoud, S., Paolucci, M., Savarimuthu, B.T.R., Verhagen, H.: Simulation and NorMAS. In: Dagstuhl Follow-Ups, vol. 4. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2013)Google Scholar
  3. 3.
    Berkes, F., Colding, J., Folke, C.: Rediscovery of traditional ecological knowledge as adaptive management. Ecol. Appl. 10(5), 1251–1262 (2000)CrossRefGoogle Scholar
  4. 4.
    Bhargava, R.: Individualism in Social Science: Forms and Limits of a Methodology. Clarendon Press, Oxford (1992)CrossRefGoogle Scholar
  5. 5.
    Bichler, G., Malm, A., Cooper, T.: Drug supply networks: a systematic review of the organizational structure of illicit drug trade. Crime Sci. 6(1), 2 (2017)CrossRefGoogle Scholar
  6. 6.
    Bosse, T., Elffers, H., Gerritsen, C., et al.: Simulating the dynamical interaction of offenders, targets and guardians. Crime Patterns Anal. 3(1), 51–66 (2010)Google Scholar
  7. 7.
    Bosse, T., Gerritsen, C., Klein, M.C.: Agent-based simulation of social learning in criminology. In: ICAART, pp. 5–13 (2009)Google Scholar
  8. 8.
    Brantingham, P., Groff, E.: The future of agent-based simulation in environmental criminology. American Society of Criminology, Nashville (2004)Google Scholar
  9. 9.
    Calvó-Armengol, A., Zenou, Y.: Social networks and crime decisions: the role of social structure in facilitating delinquent behavior. Int. Econ. Rev. 45(3), 939–958 (2004)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Modern Phys. 81(2), 591 (2009)CrossRefGoogle Scholar
  11. 11.
    Cioffi-Revilla, C.: Computational social science. Wiley Interdiscip. Rev.: Comput. Stat. 2(3), 259–271 (2010)CrossRefGoogle Scholar
  12. 12.
    Cioffi-Revilla, C.: Introduction to Computational Social Science: Principles and Applications. Springer Science & Business Media, London (2013). doi: 10.1007/978-1-4471-5661-1CrossRefzbMATHGoogle Scholar
  13. 13.
    Cohen, L.E., Felson, M.: Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44, 588–608 (1979)CrossRefGoogle Scholar
  14. 14.
    Conte, R., Paolucci, M.: On agent based modelling and computational social science. Front. Psychol. 5, 668 (2014)CrossRefGoogle Scholar
  15. 15.
    De Prisco, R., Esposito, A., Lettieri, N., Malandrino, D., Pirozzi, D., Zaccagnino, G., Zaccagnino, R.: Music plagiarism at a glance: metrics of similarity and visualizations. In: 21th International Conference Information Visualisation, IV 2017. London South Bank University, London (2017)Google Scholar
  16. 16.
    De Prisco, R., Zaccagnino, G., Zaccagnino, R.: A multi-objective differential evolution algorithm for 4-voice compositions. In: 2011 IEEE Symposium on Differential Evolution, SDE 2011, Paris, France, 11–15 April 2011, pp. 65–72 (2011); De Prisco, R., Zaccagnino, G., Zaccagnino, R.: A genetic algorithm for dodecaphonic compositions. In: Di Chio, C., et al. (eds.) EvoApplications 2011, Part II. LNCS, vol. 6625, pp. 244–253. Springer, Heidelberg (2011)Google Scholar
  17. 17.
    Décary-Hétu, D., Dupont, B.: The social network of hackers. Global Crime 13(3), 160–175 (2012)CrossRefGoogle Scholar
  18. 18.
    Della Porta, D., Keating, M.: Approaches and Methodologies in the Social Sciences: A Pluralist Perspective. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  19. 19.
    Eckstein, H.: Unfinished business reflections on the scope of comparative politics. Comp. Polit. Stud. 31(4), 505–534 (1998)CrossRefGoogle Scholar
  20. 20.
    Epstein, J.M., Axtell, R.: Growing Artificial Societies: Social Science From the Bottom Up. Brookings Institution Press, Washington, DC (1996)CrossRefGoogle Scholar
  21. 21.
    Felson, M., Clarke, R.V.: Opportunity makes the thief (1998)Google Scholar
  22. 22.
    Ferrara, E., De Meo, P., Catanese, S., Fiumara, G.: Detecting criminal organizations in mobile phone networks. Expert Syst. Appl. 41(13), 5733–5750 (2014)CrossRefGoogle Scholar
  23. 23.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)CrossRefGoogle Scholar
  24. 24.
    Furtado, V., Melo, A., Coelho, A.L., Menezes, R., Belchior, M.: Simulating crime against properties using swarm intelligence and social networks. In: Artificial Crime Analysis Systems, pp. 300–318 (2008)Google Scholar
  25. 25.
    Gilbert, N., Troitzsch, K.: Simulation for the Social Scientist. McGraw-Hill Education, New York (2005)Google Scholar
  26. 26.
    Groff, E., Mazerolle, L.: Simulated experiments and their potential role in criminology and criminal justice. Exp. Criminol. 4(3), 187–193 (2008)CrossRefGoogle Scholar
  27. 27.
    Groff, E.R.: Simulation for theory testing and experimentation: an example using routine activity theory and street robbery. J. Quant. Criminol. 23(2), 75–103 (2007)CrossRefGoogle Scholar
  28. 28.
    Hofstadter, D.R.: Gödel, escher, bach. Un eterno y grácil bucle (1980)Google Scholar
  29. 29.
    Lettieri, N., Malandrino, D., Vicidomini, L.: By investigation, I mean computation. Trends Organ. Crime 20, 31–54 (2017)CrossRefGoogle Scholar
  30. 30.
    Lettieri, N., et al.: Text and (social) network analysis as investigative tools: a case study. Inform. Diritt. 22(1), 263–280 (2013)Google Scholar
  31. 31.
    Liu, L.: Artificial crime analysis systems: using computer simulations and geographic information systems: using computer simulations and geographic information systems. IGI Global (2008)Google Scholar
  32. 32.
    Mayhew, B.H.: Structuralism versus individualism: Part II, ideological and other obfuscations. Soc. Forces 59, 627–648 (1981)CrossRefGoogle Scholar
  33. 33.
    Ormerod, P., Wiltshire, G.: ‘Binge’ drinking in the UK: a social network phenomenon. Mind Soc. 8(2), 135 (2009)CrossRefGoogle Scholar
  34. 34.
    Punzo, V.: How crime spreads through imitation in social networks: a simulation model. In: Cecconi, F. (ed.) New Frontiers in the Study of Social Phenomena, pp. 169–190. Springer, Cham (2016). doi: 10.1007/978-3-319-23938-5_10CrossRefGoogle Scholar
  35. 35.
    Sil, R.: The foundations of eclecticism the epistemological status of agency, culture, and structure in social theory. J. Theor. Polit. 12(3), 353–387 (2000)CrossRefGoogle Scholar
  36. 36.
    Squazzoni, F.: The micro-macro link in social simulation. Sociologica 2(1), 1–26 (2008)Google Scholar
  37. 37.
    Sutherland, E.H., Cressey, D.R.: Principles of Criminology. Lippincott, Philadelphia (1947)Google Scholar
  38. 38.
    Teddlie, C., Tashakkori, A.: Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage, Thousand Oaks (2009)Google Scholar
  39. 39.
    Wikström, P.O.H.: Individuals, settings, and acts of crime: situational mechanisms and the explanation of crime. The explanation of crime: context, mechanisms and development, pp. 61–107 (2006)Google Scholar
  40. 40.
    Xu, J., Chen, H.: Criminal network analysis and visualization. Commun. ACM 48(6), 100–107 (2005)CrossRefGoogle Scholar
  41. 41.
    Zhou, T., Lü, L.: Link prediction in complex networks: a survey. Phys. A: Stat. Mech. Appl. 390(6), 1150–1170 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nicola Lettieri
    • 1
    Email author
  • Antonio Altamura
    • 2
  • Delfina Malandrino
    • 2
  • Valentina Punzo
    • 1
  1. 1.INAPPRomeItaly
  2. 2.Department of Computer ScienceUniversity of SalernoFiscianoItaly

Personalised recommendations