Skip to main content

Advertisement

SpringerLink
Log in

Illicit Network Dynamics: The Formation and Evolution of a Drug Trafficking Network

  • Original Paper
  • Published:
Journal of Quantitative Criminology Aims and scope Submit manuscript

Abstract

Objectives

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?

Method

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.

Results

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.

Conclusions

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Roles included resource provider, labourer, cook, manager, dealer, security, corrupt official.

  2. A number of longitudinal models for networks have been proposed. We chose here a continuous-time framework rather than the discrete-time model proposed in Robins and Pattison (2001) and elaborated in Krackhardt and Handcock (2007) and Hanneke and Xing (2007). Snijders et al. (2010) discuss the principled differences between modelling tie-change in continuous- and discrete-time. This is further elaborated in Block et al. (2018). Furthermore, we chose an actor-oriented framework over a tie-based model. Block et al. (2017) provide a thorough comparison and a number of clarifications as to the differences and similarities between tie-based and actor-based models.

  3. Currently missing ties at the start of a period are treated in a close approximation to this scheme. See further in Ripley et al. 2017. For the first observation Krause et al. (2017) follow Koskinen et al. (2015) and assume an exponential random graph for the ties in order to account for missings. Missingness in subsequent observations is handled using multiple imputation and not, as here, treated in a fully Bayesian way.

  4. Jaccard indices are used to measure the amount of change from one network panel to the next. Higher values are indicative of greater stability.

  5. Referred to as “Dyadic mechanisms” in the sequel.

  6. Referred to as “Endogenous dynamics” in the sequel.

  7. The results of Model 5 remain largely unchanged when an effect is added for the ‘popularity’ of the focal actors, suggesting that the other effects are robust to the centrality of the individuals whose cases-filed data were extracted from.

References

  • Baker WE, Faulkner RR (1993) The social organization of conspiracy: illegal networks in the heavy electrical equipment industry. Am Sociol Rev 58(6):837–860

    Article  Google Scholar 

  • Bakker RM, Raab J, Milward HB (2012) A preliminary theory of dark network resilience. J Policy Anal Manag 31(1):33–62

    Article  Google Scholar 

  • 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 111–161

    Google Scholar 

  • Bichler G, Malm A (2013) Small arms, big guns: a dynamic model of illicit market opportunity. Global Crime 14(2–3):261–286

    Article  Google Scholar 

  • Bichler G, Malm AE (eds) (2015) Disrupting criminal networks: network analysis in crime prevention. First Forum Press

  • Bichler M, Malm A, Cooper T (2017) Drug supply networks: a systematic review of the organizational structure of illicit drug trade. Crime Sci 6:2

    Article  Google Scholar 

  • Block P, Stadtfeld C, Snijders T (2017) Forms of dependence comparing SAOMs and ERGMs from basic principles. Sociol Methods Res 1–38

  • Block P, Koskinen J, Stadtfeld CJ, Hollway J, Steglich C (2018) Change we can believe in: comparing longitudinal network models on consistency, interpretability and predictive power. Soc Netw 52:180–191

    Article  Google Scholar 

  • Bouchard M (2007) On the resilience of illegal drug markets. Global Crime 8(4):325–344

    Article  Google Scholar 

  • Bright DA, Delaney JJ (2013) Evolution of a drug trafficking network: mapping changes in network structure and function across time. Global Crime 14:238–260

    Article  Google Scholar 

  • Bright DA, Hughes CE, Chalmers J (2012) Illuminating dark networks: a social network analysis of an Australian drug trafficking syndicate. Crime Law Soc Change 57:151–176

    Article  Google Scholar 

  • Bright DA, Greenhill C, Reynolds M, Ritter A, Morselli C (2015) The use of actor-level attributes to identify key actors: a case study of an Australian drug trafficking network. J Contemp Crim Justice 31:262–278

    Article  Google Scholar 

  • Bright DA, Greenhill C, Britz T, Ritter A, Morselli C (2017) Criminal network vulnerabilities and adaptations. Global Crime 18(4):424–441

    Article  Google Scholar 

  • Broccatelli C, Everett M, Koskinen J (2016) Temporal dynamics in covert networks. Methodol Innov 9:2059799115622766

    Google Scholar 

  • Calderoni F, Skillicorn D, Zheng Q (2014) Inductive discovery of criminal group structure using spectral embedding. Inf Secur 31(1):49

    Google Scholar 

  • Carley KM, Lee JS, Krackhardt D (2002) Destabilizing networks. Connections 24(3):79–92

    Google Scholar 

  • Cockbain E, Brayley H, Laycock G (2011) Exploring internal child sex trafficking networks using social network analysis. Policing 5(2):144–157

    Article  Google Scholar 

  • Crenshaw M (2010) Mapping terrorist organizations. Unpublished working paper

  • Crossley N, Edwards G, Harries E, Stevenson R (2012) Covert social movement networks and the secrecy-efficiency trade off: the case of the UK suffragettes (1906–1914). Soc Netw 34(4):634–644

    Article  Google 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. Sage

  • de Solla Price D (1976) A general theory of bibliometric and other advantage processes. Am Soc Inf Sci 27:292–306

    Article  Google Scholar 

  • Dijkstra JK, Lindenberg S, Veenstra R, Steglich C, Isaacs J, Card NA, Hodges EV (2010) Influence and selection processes in weapon carrying during adolescence: the roles of status, aggression, and vulnerability. Criminology 48(1):187–220

    Article  Google 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 2017

  • 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 1–17

    Google Scholar 

  • Duijn PA, Kashirin V, Sloot PM (2014) The relative ineffectiveness of criminal network disruption. Sci Rep 4:4238

    Article  Google Scholar 

  • Enders W, Su X (2007) Rational terrorists and optimal network structure. J Conflict Resolut 51(1):33–57

    Article  Google Scholar 

  • Everton SF (2009) Network topography, key players and terrorist networks. Terrorist Networks 32(1):12–19

    Google Scholar 

  • Frank O, Strauss D (1986) Markov graphs. J Am Stat Assoc 81(395):832–842

    Article  Google Scholar 

  • Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1:215–239

    Article  Google Scholar 

  • Gambetta D (2009) Codes of the underworld: how criminals communicate. Princeton University Press, Princeton

    Book  Google Scholar 

  • Gilroy H (2013) Insights into terrorism: an applied exploratory research method for aberrant organisations. Ph.D., UNSW

  • Gottfredson MR, Hirschi T (1990) A general theory of crime. Stanford University Press, Stanford

    Google Scholar 

  • Grund T, Densley J (2015) Ethnic homophily and triad closure: mapping internal gang structure using exponential random graph models. J Contemp Crim Justice 31(3):354–370

    Article  Google Scholar 

  • Grund T, Morselli C (2017) Overlapping crime: stability and specialization of co-offending relationships. Soc Netw 51:14–22

    Article  Google 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 115–125

    Google Scholar 

  • Hedström P, Swedberg R (1998) Social mechanisms: an analytical approach to social theory. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Holland PW, Leinhardt S (1977) A dynamic model for social networks†. J Math Sociol 5(1):5–20

    Article  Google Scholar 

  • Kleemans ER, Van De Bunt HG (1999) The social embeddedness of organized crime. Transnatl Organ Crime 5(1):19–36

    Google Scholar 

  • Koskinen JH, Snijders TA (2007) Bayesian inference for dynamic social network data. J Stat Plan Inference 137(12):3930–3938

    Article  Google Scholar 

  • Koskinen JH, Robins GL, Wang P, Pattison PE (2013) Bayesian analysis for partially observed network data, missing ties, attributes and actors. Soc Netw 35(4):514–527

    Article  Google Scholar 

  • Koskinen J, Caimo A, Lomi A (2015) Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: an application to Foreign Direct Investments. Netw Sci 3(1):58–77

    Article  Google 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 14–27

    Google Scholar 

  • Krause RW, Huisman M, Snijders TAB (2017) Multiple imputation for longitudinal network data. Ital J Appl Stat (Forthcoming)

  • la Haye K, Green HD, Kennedy DP, Pollard MS, Tucker JS (2013) Selection and influence mechanisms associated with marijuana initiation and use in adolescent friendship networks. J Res Adolesc 23(3):474–486

    Article  Google Scholar 

  • Li L, Alderson D, Doyle JC, Willinger W (2005) Towards a theory of scale-free graphs: definition, properties, and implications. Internet Math 2(4):431–523

    Article  Google Scholar 

  • Lusher D, Koskinen J, Robins GL (2013) Exponential random graph models for social networks: theory, methods, and applications. Cambridge University Press, Cambridge

    Google 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):264–282

    Google Scholar 

  • Malm A, Bichler G (2011) Networks of collaborating criminals: assessing the structural vulnerability of drug markets. J Res Crime Delinq 48(2):271–297

    Article  Google Scholar 

  • Malm A, Bichler G (2013) Using friends for money: the positional importance of money-launderers in organized crime. Trends Organ Crime 16:365–381

    Article  Google Scholar 

  • McCarty C (2002) Structure in personal networks. J Soc Struct 3(1):20

    Google Scholar 

  • McCarty C, Wutich A (2005) Conceptual and empirical arguments for including or excluding ego from structural analyses of personal networks. Connections 26(2):82–88

    Google Scholar 

  • McCulloh I, Carley KM (2011) Detecting change in longitudinal social networks. J Soc Struct 12:1–37

    Google Scholar 

  • Merton R (1968) The Matthew effect in science. Science 159:56–63

    Article  Google Scholar 

  • Milward BH, Raab J (2006) Dark networks as organizational problems: elements of a theory 1. Int Public Manag J 9(3):333–360

    Article  Google Scholar 

  • Morselli C (2009a) Hells Angels in springtime. Trends Organ Crime 12:145–158

    Article  Google Scholar 

  • Morselli C (2009b) Inside criminal networks. Springer, New York

    Book  Google Scholar 

  • Morselli C (2010) Assessing vulnerable and strategic positions in a criminal network. J Contemp Crim Justice 26:382–392

    Article  Google Scholar 

  • Morselli C, Petit K (2007) Law-enforcement disruption of a drug importation network. Global Crime 8(2):109–130

    Article  Google Scholar 

  • Morselli C, Roy J (2008) Brokerage qualifications in ringing operations. Criminology 46(1):28

    Article  Google Scholar 

  • Morselli C, Giguère C, Petit K (2007) The efficiency/security trade-off in criminal networks. Soc Netw 29(1):143–153

    Article  Google 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):171–192

    Google Scholar 

  • Oliver K, Crossley N, Edwards G, Koskinen J, Everett M (2014). Covert networks: structures, processes and types. University of Manchester, Manchester, UK

    Google 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 287–304

  • Raab J, Milward HB (2003) Dark networks as problems. J Public Adm Theory 13:413–439

    Article  Google 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)

  • Robins GL, Pattison PE (2001) Random graph models for temporal processes in social networks. J Math Sociol 25:5–41

    Article  Google Scholar 

  • Robins G (2009) Understanding individual behaviors within covert networks: the interplay of individual qualities, psychological predispositions, and network effects. Trends Organ Crime 12(2):166–187

    Article  Google Scholar 

  • Rubin DB (1976) Inference and missing data (with discussion). Biometrika 63:581–592

    Article  Google Scholar 

  • Ruef M, Aldrich HE, Carter NM (2003) The structure of founding teams: Homophily, strong ties, and isolation among U.S. enterpeneurs. Am Sociol Rev 68:195–222

    Article  Google Scholar 

  • Snijders TA (2001) The statistical evaluation of social network dynamics. Sociol Methodol 31(1):361–395

    Article  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 221–247

    Google Scholar 

  • Snijders TA, Pattison PE, Robins GL, Handcock MS (2006) New specifications for exponential random graph models. Sociol Methodol 36(1):99–153

    Article  Google Scholar 

  • Snijders TAB, Koskinen JH, Schweinberger M (2010) Maximum likelihood estimation for social network dynamics. Ann Appl Stat 4:567–588

    Article  Google Scholar 

  • Stevenson R, Crossley N (2014) Change in covert social movement networks: The ‘Inner Circle’of the provisional Irish Republican Army. Soc Mov Stud 13(1):70–91

    Article  Google Scholar 

  • Tenti V, Morselli C (2014) Group co-offending networks in Italy’s illegal drug trade. Crime Law Soc Change 62(1):21–44

    Article  Google Scholar 

  • Turanovic JJ, Young JT (2016) Violent offending and victimization in adolescence: social network mechanisms and homophily. Criminology 54(3):487–519

    Article  Google Scholar 

  • Varese F (2011) Mafias on the move: How organized crime conquers new territories. Princeton University Press

  • Varese F (2012) The structure and the content of criminal connections: the Russian Mafia in Italy. Eur Sociol Rev 29:899–909

    Article  Google Scholar 

  • Von Lampe K, Ole Johansen P (2004) Organized crime and trust: on the conceptualization and empirical relevance of trust in the context of criminal networks. Global Crime 6(2):159–184

    Article  Google Scholar 

  • Wasserman S (1980) A stochastic model for directed graphs with transition rates determined by reciprocity. Sociol Methodol 11:392–412

    Article  Google 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, Cambridge

    Google Scholar 

  • Wood G (2017) The structure and vulnerability of a drug trafficking collaboration network. Soc Netw 48:1–9

    Article  Google Scholar 

  • Xing EP, Fu W, Song L (2010) A state-space mixed membership blockmodel for dynamic network tomography. Ann Appl Stat 4(2):535–566

    Article  Google 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 359–377

Download references

Acknowledgements

Johan Koskinen's work was supported by Leverhulme Trust (RPG-2013-140) and BA/Leverhulme SRG 2012.

Author information

Authors and Affiliations

  1. Centre for Crime Policy and Research, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia

    David Bright

  2. Mitchell Centre of Social Network Analysis and Department of Social Statistics, The University of Manchester, Manchester, UK

    Johan Koskinen

  3. Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, 3010, Australia

    Johan Koskinen

  4. Institute of Analytical Sociology, University of Linkoping, Linköping, 58183, Sweden

    Johan Koskinen

  5. California State University, Long Beach, Long Beach, USA

    Aili Malm

Authors
  1. David Bright
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johan Koskinen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aili Malm
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Bright.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bright, D., Koskinen, J. & Malm, A. Illicit Network Dynamics: The Formation and Evolution of a Drug Trafficking Network. J Quant Criminol 35, 237–258 (2019). https://doi.org/10.1007/s10940-018-9379-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-018-9379-8

Keywords

Search

Navigation