Illicit Network Dynamics: The Formation and Evolution of a Drug Trafficking Network
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The project aims to: (1) investigate structural and functional changes in an Australian drug trafficking network across time to determine ways in which such networks form and evolve. To meet this aim, the project will answer the following research questions: (1) What social structural changes occur in drug trafficking networks across time? (2) How are these structural changes related to roles/tasks performed by network members? (3) What social processes can account for change over time in drug trafficking networks?
The relational data on the network was divided into four two years periods. Actors were allocated to specific roles. We applied a stochastic actor-oriented model to explain the dynamics of the network across time. Using RSiena, we estimated a number of models with the key objectives of investigating: (1) the effect of roles only; (2) the endogenous effect of degree-based popularity (Matthew effect); (3) the endogenous effect of balancing connectivity with exposure (preference for indirect rather than direct connections); (4) how degree-based popularity is moderated by tendencies towards reach and exposure.
Preferential attachment is completely moderated by a preference for having indirect ties, meaning that centralization is a result of actors preferring indirect connections to many others and not because of a preference for connecting to popular actors. Locally, actors seek cohesive relationships through triadic closure.
Actors do not seek to create an efficient network that is highly centralized at the expense of security. Rather, actors strive to optimize security through triadic closure, building trust, and protecting themselves and actors in close proximity through the use of brokers that offer access to the rest of the network.
KeywordsDrug trafficking Social network analysis Dynamic Stochastic actor-oriented models Longitudinal social network analysis
Johan Koskinen's work was supported by Leverhulme Trust (RPG-2013-140) and BA/Leverhulme SRG 2012.
- Bales R (1953) The equilibrium problem in small groups. In: Parsons T, Bales R, Shils E (eds) Working papers in the theory of action. Free Press, Glencoe, pp 111–161Google Scholar
- Bichler G, Malm AE (eds) (2015) Disrupting criminal networks: network analysis in crime prevention. First Forum PressGoogle Scholar
- Block P, Stadtfeld C, Snijders T (2017) Forms of dependence comparing SAOMs and ERGMs from basic principles. Sociol Methods Res 1–38Google Scholar
- Broccatelli C, Everett M, Koskinen J (2016) Temporal dynamics in covert networks. Methodol Innov 9:2059799115622766Google Scholar
- Calderoni F, Skillicorn D, Zheng Q (2014) Inductive discovery of criminal group structure using spectral embedding. Inf Secur 31(1):49Google Scholar
- Carley KM, Lee JS, Krackhardt D (2002) Destabilizing networks. Connections 24(3):79–92Google Scholar
- Crenshaw M (2010) Mapping terrorist organizations. Unpublished working paperGoogle Scholar
- Crossley N, Bellotti E, Edwards G, Everett MG, Koskinen J, Tranmer M (2015) Social network analysis for ego-nets: social network analysis for actor-centred networks. SageGoogle Scholar
- Diviak T, Dijkstra JK, Snijders TAB (2017) The efficiency/security trade-off: testing a theory on criminal networks. Paper presented at the 9th Illicit Networks Workshop, Flinders University, Australia, 11–13 December 2017Google Scholar
- Doreian P, Stokman FN (1997) The dynamics and evolution of social networks. In: Doreian P, Stokman FN (eds) Evolution of social networks. Gordon and Breach, New York, pp 1–17Google Scholar
- Everton SF (2009) Network topography, key players and terrorist networks. Terrorist Networks 32(1):12–19Google Scholar
- Gambetta D (2009) Codes of the underworld: how criminals communicate. Princeton University Press, PrincetonGoogle Scholar
- Gilroy H (2013) Insights into terrorism: an applied exploratory research method for aberrant organisations. Ph.D., UNSWGoogle Scholar
- Gottfredson MR, Hirschi T (1990) A general theory of crime. Stanford University Press, StanfordGoogle Scholar
- Hanneke S, Xing EP (2007) Discrete temporal models of social networks. In: Airoldi E, Blei DM, Fienberg SE, Goldenberg A, Xing EP, Zheng AX (eds) Statistical network analysis: models, issues and new directions (ICML 2006). Lecture notes in computer science 4503. Springer, Berlin, pp 115–125Google Scholar
- Krackhardt D, Handcock MS (2007) Heider vs Simmel: emergent features in dynamic structures. In: Airoldi E, Blei DM, Fienberg SE, Goldenberg A, Xing EP, Zheng AX (eds) Statistical network analysis: models, issues and new directions (ICML 2006). Lecture notes in computer science 4503. Springer, Berlin, pp 14–27Google Scholar
- Krause RW, Huisman M, Snijders TAB (2017) Multiple imputation for longitudinal network data. Ital J Appl Stat (Forthcoming)Google Scholar
- Lusher D, Koskinen J, Robins GL (2013) Exponential random graph models for social networks: theory, methods, and applications. Cambridge University Press, CambridgeGoogle Scholar
- Mainas ED (2012) The analysis of criminal and terrorist organisations as social network structures: a quasi-experimental study. Int J Police Sci Manag 14(3):264–282Google Scholar
- McCarty C (2002) Structure in personal networks. J Soc Struct 3(1):20Google Scholar
- McCarty C, Wutich A (2005) Conceptual and empirical arguments for including or excluding ego from structural analyses of personal networks. Connections 26(2):82–88Google Scholar
- McCulloh I, Carley KM (2011) Detecting change in longitudinal social networks. J Soc Struct 12:1–37Google Scholar
- Natarajan M (2006) Understanding the structure of a large heroin distribution network: a quantitative analysis of qualitative data. Underst Struct Large Heroin Distrib Netw Quant Anal Qual Data 22(2):171–192Google Scholar
- Oliver K, Crossley N, Edwards G, Koskinen J, Everett M (2014). Covert networks: structures, processes and types. University of Manchester, Manchester, UKGoogle Scholar
- Qin J, Xu JJ, Hu D, Sageman M, Chen H (2005, May) Analyzing terrorist networks: a case study of the global salafi jihad network. In: International conference on Intelligence and security Informatics. Springer, Berlin, Heidelberg, pp 287–304Google Scholar
- Ripley RM, Snijders TA, Boda Z, Vörös A, Preciado P (2017) Manual for RSIENA. University of Oxford, Department of Statistics; Nuffield College. (RSiena version 1.1-307 manual version, 12 May)Google Scholar
- Snijders, TAB, Pickup M (2017) Stochastic actor-oriented models for network dynamics. In: Victor JN, Montgomery AH, Lubell M (ed) Oxford handbook of political networks. Oxford University Press, Oxford, pp 221–247Google Scholar
- Varese F (2011) Mafias on the move: How organized crime conquers new territories. Princeton University PressGoogle Scholar
- Wasserman S, Scott J, Carrington PJ (2005) Introduction. In: Scoft J, Carrington PJ, Wasserman S (eds) Models and methods in social network analysis. Cambridge University Press, CambridgeGoogle Scholar
- Xu J, Marshall B, Kaza S, Chen H (2004, June) Analyzing and visualizing criminal network dynamics: a case study. In: International conference on intelligence and security informatics, Springer, Berlin, Heidelberg, pp 359–377Google Scholar