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Operations Engineering & Analytics

A review of research in illicit supply-chain networks and new directions to thwart them

ORCID Icon, ORCID Icon & ORCID Icon
Pages 134-158 | Received 27 Nov 2020, Accepted 26 May 2021, Published online: 06 Aug 2021

References

  • Agreste, S., Catanese, S., de Meo, P., Ferrara, E. and Fiumara, G. (2016) Network structure and resilience of mafia syndicates. Information Sciences, 351, 30–47.
  • Al Hasan, M. and Zaki, M.J. (2011) A survey of link prediction in social networks, in Social Network Data Analytics, Springer, Boston, MA, pp. 243–275.
  • Albert, L.A., Nikolaev, A., Lee, A.J., Fletcher, K. and Jacobson, S.H. (2021) A review of risk-based security and its impact on TSA PreCheck. IISE Transactions, 53, 657–670.
  • Albright, D., Brannan, P. and Stricker, A.S. (2010) Detecting and disrupting illicit nuclear trade after A.Q. Khan. The Washington Quarterly, 33, 85–106.
  • Anwar, T. and Abulaish, M. (2014) A social graph based text mining framework for chat log investigation. Digital Investigation, 11, 349–362.
  • Arroyave, F.J., Petersen, A.M., Jenkins, J. and Hurtado, R. (2020) Multiplex networks reveal geographic constraints on illicit wildlife trafficking. Applied Network Science, 5, 20.
  • Bahulkar, A., Baycik, N.O., Sharkey, T., Shen, Y., Szymanski, B. and Wallace, W. (2018a) Integrative analytics for detecting and disrupting transnational interdependent criminal smuggling, money, and money-laundering networks, in 2018 IEEE International Symposium on Technologies for Homeland Security., IEEE Press, Piscataway, NJ, pp. 1–6.
  • Bahulkar, A., Szymanski, B.K., Baycik, N.O. and Sharkey, T.C. (2018b) Community detection with edge augmentation in criminal networks, in 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining., IEEE Press, Piscataway, NJ, pp. 1168–1175.
  • Baker, W.E. and Faulkner, R.R. (1993) The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry. American Sociological Review, 58, 837–860.
  • Bak Ir, N.O. (2008) A decision tree model for evaluating countermeasures to secure cargo at United States southwestern ports of entry. Decision Analysis, 5, 230–248.
  • Bakker, C., Webster, J., Nowak, K.E., Chatterjee, S., Perkins, C.J. and Brigantic, R. (2020) Multi-game modeling for counter-smuggling. Reliability Engineering & System Safety, 200, 106958.
  • Ballester, C., Calvó-Armengol, A. and Zenou, Y. (2006) Who’s who in networks. Wanted: The key player. Econometrica, 74, 1403–1417.
  • Barabási, A.-L. and Albert, R. (1999) Emergence of scaling in random networks. Science, 286, 509–512.
  • Basu, G. (2013) The role of transnational smuggling operations in illicit supply chains. Journal of Transportation Security, 6, 315–328.
  • Basu, G. (2014a) Combating illicit trade and transnational smuggling: Key challenges for customs and border control agencies. World Customs Journal, 8, 15–26.
  • Basu, G. (2014b) Concealment, corruption, and evasion: A transaction cost and case analysis of illicit supply chain activity. Journal of Transportation Security, 7, 209–226.
  • Baveja, A., Batta, R., Caulkins, J.P. and Karwan, M.H. (1993) Modeling the response of illicit drug markets to local enforcement. Socio-Economic Planning Sciences, 27, 73–89.
  • Baveja, A., Feichtinger, G., Hartl, R., Haunschmied, J. and Kort, P. (2000) A resource-constrained optimal control model for crackdown on illicit drug markets. Journal of Mathematical Analysis and Applications, 249, 53–79.
  • Baveja, A., Jamil, M. and Kushary, D. (2004) A sequential model for cracking down on street markets for illicit drugs. Socio-Economic Planning Sciences, 38, 7–41.
  • Baycik, N.O., Sharkey, T.C. and Rainwater, C.E. (2018) Interdicting layered physical and information flow networks. IISE Transactions, 50, 316–331.
  • Baycik, N.O., Sharkey, T.C. and Rainwater, C.E. (2020) A Markov decision process approach for balancing intelligence and interdiction operations in city-level drug trafficking enforcement. Socio-Economic Planning Sciences, 69, 100700.
  • Beckert, J. and Wehinger, F. (2013) In the shadow: Illegal markets and economic sociology. Socio-Economic Review, 11, 5–30.
  • Becucci, S. (2004) Old and new actors in the Italian drug trade: Ethnic succession or functional specialization? European Journal on Criminal Policy and Research, 10, 257–283.
  • Bianconi, G. (2013) Statistical mechanics of multiplex networks: Entropy and overlap. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 87, 062806.
  • Bichler, G., Malm, A. and Cooper, T. (2017) Drug supply networks: A systematic review of the organizational structure of illicit drug trade. Crime Science, 6, 2.
  • Bindu, P., Thilagam, P.S. and Ahuja, D. (2017) Discovering suspicious behavior in multilayer social networks. Computers in Human Behavior, 73, 568–582.
  • Boivin, R. (2013) Drug trafficking networks in the world economy, in Crime and Networks, Routledge New York, NY, pp. 12–194.
  • Borgatti, S.P. (2006) Identifying sets of key players in a social network. Computational and Mathematical Organization Theory, 12, 21–34.
  • Borgatti, S.P. and Everett, M.G. (2000) Models of core/periphery structures. Social Networks, 21, 375–395.
  • Borgatti, S.P. and Everett, M.G. (2006) A graph-theoretic perspective on centrality. Social Networks, 28, 466–484.
  • Boros, E., Fedzhora, L., Kantor, P., Saeger, K. and Stroud, P. (2009) A large-scale linear programming model for finding optimal container inspection strategies. Naval Research Logistics (NRL), 56, 404–420.
  • Bouchard, M. (2007) On the resilience of illegal drug markets. Global Crime, 8, 325–344.
  • Bradshaw, H. (2016) Glowing pockets: Modeling illicit nuclear and radiological trafficking networks in the former Soviet Union, in 2016 National Conference on Undergraduate Research.
  • Bright, D., Greenhill, C., Britz, T., Ritter, A. and Morselli, C. (2017) Criminal network vulnerabilities and adaptations. Global Crime, 18, 424–441.
  • Bright, D., Koskinen, J. and Malm, A. (2019) Illicit network dynamics: The formation and evolution of a drug trafficking network. Journal of Quantitative Criminology, 35, 237–258.
  • Bright, D.A. (2015) Disrupting and dismantling dark networks: Lessons from social network analysis and law enforcement simulations, in Illuminating Dark Networks: The Study of Clandestine Groups and Organizations., Cambridge University Press New York, NY, pp. 39–51.
  • Bright, D.A. and Delaney, J.J. (2013) Evolution of a drug trafficking network: Mapping changes in network structure and function across time. Global Crime, 14, 238–260.
  • Bright, D.A., Greenhill, C., Reynolds, M., Ritter, A. and Morselli, C. (2015) The use of actor-level attributes and centrality measures to identify key actors: A case study of an Australian drug trafficking network. Journal of Contemporary Criminal Justice, 31, 262–278.
  • Bright, D.A., Hughes, C.E. and Chalmers, J. (2012) Illuminating dark networks: A social network analysis of an Australian drug trafficking syndicate. Crime, Law and Social Change, 57, 151–176.
  • Broccatelli, C., Everett, M. and Koskinen, J. (2016) Temporal dynamics in covert networks. Methodological Innovations, 9, 205979911562276–205979911562214.
  • Brown, G.F. and Silverman, L.P. (1980) The retail price of heroin: Estimation and applications, in Quantitative Explorations in Drug Abuse Policy, Springer, Dordrecht, pp. 25–53.
  • Brown, S.S. and Hermann, M.G. (2020) Financing the illicit economy. In Transnational Crime and Black Spots, International Political Economy Series. Palgrave Macmillan, London, pp. 111–139.
  • Cai, P., Cai, J.-Y. and Naik, A.V. (1998) Efficient algorithms for a scheduling problem and its applications to illicit drug market crackdowns. Journal of Combinatorial Optimization, 1, 367–376.
  • Calderoni, F. (2012) The structure of drug trafficking mafias: The Ndrangheta and cocaine. Crime, Law and Social Change, 58, 321–349.
  • Calderoni, F., Catanese, S., de Meo, P., Ficara, A. and Fiumara, G. (2020) Robust link prediction in criminal networks: A case study of the Sicilian mafia. Expert Systems with Applications, 161, 113666.
  • Calderoni, F. and Piccardi, C. (2014) Uncovering the structure of criminal organizations by community analysis: The infinito network, in 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems. IEEE Press, Piscataway, NJ, pp. 301–308.
  • Camossi, E., Dimitrova, T. and Tsois, A. (2012) Detecting anomalous maritime container itineraries for anti-fraud and supply chain security, in 2012 European Intelligence and Security Informatics Conference, IEEE Press, Piscataway, NJ, pp. 76–83.
  • Campana, P. and Varese, F. (in press) Studying organized crime networks: Data sources, boundaries and the limits of structural measures. Social Networks.
  • Carley, K.M. (2006) Destabilization of covert networks. Computational and Mathematical Organization Theory, 12, 51–66.
  • Carley, K.M., Dombroski, M., Tsvetovat, M., Reminga, J. and Kamneva, N. (2003) Destabilizing dynamic covert networks, in Proceedings of the 8th International Command and Control Research and Technology Symposium, pp. 79–92.
  • Carley, K.M., Lee, J.-S. and Krackhardt, D. (2002) Destabilizing networks. Connections, 24, 79–92.
  • Caulkins, J.P. (1993) Local drug markets’ response to focused police enforcement. Operations Research, 41, 848–863.
  • Caulkins, J.P. (1995) Domestic geographic variation in illicit drug prices. Journal of Urban Economics, 37, 38–56.
  • Caulkins, J.P. (1997) Modeling the domestic distribution network for illicit drugs. Management Science, 43, 1364–1371.
  • Caulkins, J.P. and Baker, D. (2010) Cobweb dynamics and price dispersion in illicit drug markets. Socio-Economic Planning Sciences, 44, 220–230.
  • Caulkins, J.P., Crawford, G. and Reuter, P. (1993) Simulation of adaptive response: A model of drug interdiction. Mathematical and Computer Modelling, 17, 37–52.
  • Caulkins, J.P., Disley, E., Tzvetkova, M., Pardal, M., Shah, H. and Zhang, X. (2016) Modeling the structure and operation of drug supply chains: The case of cocaine and heroin in Italy and Slovenia. The International Journal on Drug Policy, 31, 64–73.
  • Caulkins, J.P. and Hao, H. (2008) Modelling drug market supply disruptions: Where do all the drugs not go? Journal of Policy Modeling, 30, 251–270.
  • Caulkins, J.P. and Padman, R. (1993) Interdiction’s impact on the structure and behavior of the export-import sector for illicit drugs. Zor - Methods and Models of Operations Research, 37, 207–224.
  • Cavallaro, L., Ficara, A., de Meo, P., Fiumara, G., Catanese, S., Bagdasar, O., Song, W. and Liotta, A. (2020) Disrupting resilient criminal networks through data analysis: The case of Sicilian mafia. Plos One, 15, e0236476.
  • Cedillo-Campos, M.G., Sánchez-Ramírez, C., Vadali, S., Villa, J.C. and Menezes, M.B. (2014) Supply chain dynamics and the “cross-border effect”: The US–Mexican border’s case. Computers & Industrial Engineering, 72, 261–273.
  • Chaloupka, F.J., Edwards, S.M., Ross, H. and Diaz, M. (2015) Preventing and reducing illicit tobacco trade in the United States. Technical report, Centers for Disease Control and Prevention, Atlanta, GA.
  • Chandola, V., Banerjee, A. and Kumar, V. (2009) Anomaly detection: A survey. ACM Computing Surveys, 41, 1–58.
  • Chen, Z., Van Khoa, L.D., Teoh, E.N., Nazir, A., Karuppiah, E.K. and Lam, K.S. (2018) Machine learning techniques for anti-money laundering solutions in suspicious transaction detection: A review. Knowledge and Information Systems, 57, 245–285.
  • Cho, S.-H., Fang, X. and Tayur, S. (2015) Combating strategic counterfeiters in licit and illicit supply chains. Manufacturing & Service Operations Management, 17, 273–289.
  • Chopra, S. and Meindl, P. (2019) Supply Chain Management: Strategy, Planning & Operation. Pearson Education.
  • Clemons, E.K., Reddi, S.P. and Row, M.C. (1993) The impact of information technology on the organization of economic activity: The “move to the middle” hypothesis. Journal of Management Information Systems, 10, 9–35.
  • Clifton, K.L. and Rastogi, A. (2016) Curbing illegal wildlife trade: The role of social network analysis. Technical Report 5, International Union for the Conservation of Nature.
  • Coke-Hamilton, P. and Hardy, J. (2019, July) Illicit trade endangers the environment, the law and the sdgs. We need a global response. https://www.weforum.org/agenda/2019/07/illicit-trade-sdgs-environment-global-danger.
  • Coscia, M. and Rios, V. (2012) How and where do criminals operate? Using Google to track Mexican drug trafficking organizations. Technical report, Center for International Development at Harvard University, Cambridge, MA.
  • Coutinho, J.A., Diviák, T., Bright, D. and Koskinen, J. (2020) Multilevel determinants of collaboration between organised criminal groups. Social Networks, 63, 56–69.
  • Crane, B.D. and Rivolo, A.R. (1997) An empirical examination of counterdrug interdiction program effectiveness. Technical report, Institute for Defense Analyses, Alexandria VA.
  • Crotty, S.M. (2015) Locating day-labor employment: Toward a geographic understanding of day-labor hiring site locations in the San Diego metropolitan area. Urban Geography, 36, 993–1017.
  • Crotty, S.M. and Bouché, V. (2018) The red-light network: Exploring the locational strategies of illicit massage businesses in Houston, Texas. Papers in Applied Geography, 4, 205–227.
  • Das, K., Samanta, S. and Pal, M. (2018) Study on centrality measures in social networks: A survey. Social Network Analysis and Mining, 8, 13.
  • Davis, R.C. and Lurigio, A.J. (1996) Fighting Back: Neighborhood Antidrug Strategies. SAGE Publications, Thousand Oaks, CA.
  • Dean, G., Gottschalk, P. and Fahsing, I. (2010) Organized Crime: Policing Illegal Business Entrepreneurialism. Oxford University Press.
  • Decker, S. and Chapman, M.T. (2008) Drug Smugglers on Drug Smuggling: Lessons from the Inside. Temple University Press, Philadelphia, PA.
  • Deville, D. (2013) The illicit supply chain, in Convergence: Illicit Networks and National Security in the Age of Globalization., National Defence University Press, Washington DC, pp. 63–74.
  • Di Minin, E., Fink, C., Tenkanen, H. and Hiippala, T. (2018) Machine learning for tracking illegal wildlife trade on social media. Nature Ecology & Evolution, 2, 406–407.
  • Dicken, P. (2003) Global production networks in Europe and East Asia: The automobile components industries. Technical report, Manchester University, School of Environment and Development. GPN Working Paper 7, Manchester, UK.
  • Diesner, J. and Carley, K.M. (2004) Using network text analysis to detect the organizational structure of covert networks, in Proceedings of the North American Association for Computational Social and Organizational Science Conference.
  • Digiampietri, L.A., Roman, N.T., Meira, L.A., Ferreira, C.D., Kondo, A.A., Constantino, E.R., R.C. Brandao, B.C, R., Ribeiro, H.S. and Carolino, P.K. (2008) Uses of artificial intelligence in the Brazilian customs fraud detection system, in Proceedings of the 2008 International Conference on Digital Government Research, pp. 181–187.
  • Dijkstra, E.W. (1959) A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271.
  • Dimitrov, N.B., Michalopoulos, D.P., Morton, D.P., Nehme, M.V., Pan, F., Popova, E., Schneider, E.A. and Thoreson, G.G. (2011) Network deployment of radiation detectors with physics-based detection probability calculations. Annals of Operations Research, 187, 207–228.
  • Dimitrova, T., Tsois, A. and Camossi, E. (2014) Development of a web-based geographical information system for interactive visualization and analysis of container itineraries. International Journal of Computer and Information Technology, 3, 1–8.
  • Dittus, M., Wright, J. and Graham, M. (2018) Platform criminalism: The ‘last-mile’ geography of the darknet market supply chain, in Proceedings of the 2018 World Wide Web Conference, pp. 277–286.
  • Diviák, T., Dijkstra, J.K. and Snijders, T.A. (2019) Structure, multiplexity, and centrality in a corruption network: The Czech Rath affair. Trends in Organized Crime, 22, 274–297.
  • Dray, A., Mazerolle, L., Perez, P. and Ritter, A. (2008) Policing Australia’s ‘heroin drought’: Using an agent-based model to simulate alternative outcomes. Journal of Experimental Criminology, 4, 267–287.
  • Duijn, P.A., Kashirin, V. and Sloot, P.M. (2014) The relative ineffectiveness of criminal network disruption. Scientific Reports, 4, 4238.
  • Elsisy, A., Mandviwalla, A., Szymanski, B. and Sharkey, T. (2020) A synthetic network generator for covert network analytics. https://arxiv.org/abs/2008.04445.
  • Enayaty-Ahangar, F., Rainwater, C.E. and Sharkey, T.C. (2019) A logic-based decomposition approach for multi-period network interdiction models. Omega, 87, 71–85.
  • Enders, W. and Su, X. (2007) Rational terrorists and optimal network structure. Journal of Conflict Resolution, 51, 33–57.
  • Erdős, P. and Rényi, A. (1959) On random graphs I. Publicationes Mathematicae Debrecen, 6, 290–297.
  • Everett, M.G. and Borgatti, S.P. (1999) The centrality of groups and classes. The Journal of Mathematical Sociology, 23, 181–201.
  • Everton, S.F. (2009) Network topography, key players and terrorist networks. Connections, 32, 12–19.
  • Fanusie, Y. and Robinson, T. (2018) Bitcoin laundering: An analysis of illicit flows into digital currency services. Technical report, Center on Sanctions and Illicit Finance.
  • Farasat, A., Gross, G., Nagi, R. and Nikolaev, A.G. (2016) Social network analysis with data fusion. IEEE Transactions on Computational Social Systems, 3, 88–99.
  • Farrugia, S., Ellul, J. and Azzopardi, G. (2020) Detection of illicit accounts over the ethereum blockchain. Expert Systems with Applications, 150, 113318.
  • Feige, E.L. (1997) Underground activity and institutional change: Productive, protective and predatory behavior in transition economies, in Transforming Post-communist Political Economies, National Academy Press, Washington DC, pp. 21–34.
  • Fortunato, S. (2010) Community detection in graphs. Physics Reports, 486, 75–174.
  • Fuentes, J.R. (1998) The life of a cell: Managerial practice and strategy in Colombian cocaine distribution in the United States. Ph.D. thesis, City University of New York.
  • Gaukler, G.M., Li, C., Ding, Y. and Chirayath, S.S. (2012) Detecting nuclear materials smuggling: Performance evaluation of container inspection policies. Risk Analysis: An Official Publication of the Society for Risk Analysis, 32, 531–554.
  • Geller, A., Rizi, S.M.M. and Łatek, M.M. (2011) How corruption blunts counternarcotic policies in Afghanistan: A multiagent investigation, in International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, Springer, pp. 121–128.
  • Gera, R., Miller, R., MirandaLopez, M., Saxena, A. and Warnke, S. (2017) Three is the answer: Combining relationships to analyze multilayered terrorist networks, in 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining., IEEE Press, Piscataway, NJ, pp. 868–875.
  • Gimenez-Salinas Framis, A. (2011) Illegal networks or criminal organizations: Power, roles and facilitators in four cocaine trafficking structures, in Third Annual Illicit Networks Workshop, Montreal.
  • Giommoni, L., Aziani, A. and Berlusconi, G. (2017) How do illicit drugs move across countries? A network analysis of the heroin supply to Europe. Journal of Drug Issues, 47, 217–240.
  • Godspower-Akpomiemie, E. and Ojah, K. (2019) Money laundering, tax havens and transparency: Any role for the board of directors of banks? in Enhancing Board Effectiveness, Routledge, New York, NY, pp. 248–266.
  • González Ordiano, J.Á., Finn, L., Winterlich, A., Moloney, G. and Simske, S. (2020a) A method for estimating driving factors of illicit trade using node embeddings and clustering, Pattern Recognition, Springer International Publishing, Cham, pp. 231–241.
  • González Ordiano, J. Á., Finn, L., Winterlich, A., Moloney, G. and Simske, S. (2020b) On the analysis of illicit supply networks using variable state resolution-Markov chains, in Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer International Publishing, Cham, pp. 513–527.
  • Grant Thornton (2013) Illicit trade in Ireland: Uncovering the cost to the Irish economy. https://www.drugsandalcohol.ie/19844/1/Illicit-Trade-in-Ireland-report.pdf.
  • Grassi, R., Calderoni, F., Bianchi, M. and Torriero, A. (2019) Betweenness to assess leaders in criminal networks: New evidence using the dual projection approach. Social Networks, 56, 23–32.
  • Greenhill, K.M. (2009) Kleptocratic interdependence: Trafficking, corruption, and the marriage of politics and illicit profits, in Corruption, Global Security, and World Order, Brookings Institution Press, Washington, DC, pp. 96–123.
  • Guo, Q., An, B., Zick, Y. and Miao, C. (2016) Optimal interdiction of illegal network flow, in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 2507–2513.
  • Haas, T.C. and Ferreira, S.M. (2015) Federated databases and actionable intelligence: Using social network analysis to disrupt transnational wildlife trafficking criminal networks. Security Informatics, 4, 2.
  • Hastings, J.V. (2012) The geography of nuclear proliferation networks: The case of AQ Khan. The Nonproliferation Review, 19, 429–450.
  • Hauenstein, S., Kshatriya, M., Blanc, J., Dormann, C.F. and Beale, C.M. (2019) African elephant poaching rates correlate with local poverty, national corruption and global ivory price. Nature Communications, 10, 1–9.
  • Helbling, C.E., Kelly, C.D., Sipperly, C.D., Price, C.Z. and Schott, L.R. (2012) Modeling Honduran illicit drug networks. Technical Report, United States Military Academy, West Point, NY.
  • Hintsa, J. and Mohanty, S. (2014) A literature-based qualitative framework for assessment of socio-economic negative impacts of common illicit cross-border freight logistics flows, in Innovative Methods in Logistics and Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics, Volume 18, epubli GmbH, pp. 317–338.
  • Hirshman, J., Huang, Y. and Macke, S. (2013) Unsupervised approaches to detecting anomalous behavior in the bitcoin transaction network. Project for CS229, Stanford University, CA.
  • Hua, Z., Li, S. and Tao, Z. (2006) A rule-based risk decision-making approach and its application in China’s customs inspection decision. Journal of the Operational Research Society, 57, 1313–1322.
  • Hussain, D.A. and Ortiz-Arroyo, D. (2008) Locating key actors in social networks using Bayes’ posterior probability framework, in Intelligence and Security Informatics., Springer, Berlin Heidelberg, pp. 27–38.
  • Isah, H., Neagu, D. and Trundle, P. (2015) Bipartite network model for inferring hidden ties in crime data, in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE Press, Piscataway, NJ, pp. 994–1001.
  • Jabarzare, Z., Zolfagharinia, H. and Najafi, M. (2020) Dynamic interdiction networks with applications in illicit supply chains. Omega, 96, 102069.
  • Jacobson, S.H., Karnani, T., Kobza, J.E. and Ritchie, L. (2006) A cost-benefit analysis of alternative device configurations for aviation-checked baggage security screening. Risk Analysis: An Official Publication of the Society for Risk Analysis, 26, 297–310.
  • Johns, T. and Hayes, R. (2003) Behind the fence: Buying and selling stolen merchandise. Security Journal, 16, 29–44.
  • Kammer-Kerwick, M., Busch-Armendariz, N. and Talley, M. (2018) Disrupting illicit supply networks: New applications of operations research and data analytics to end modern slavery. Technical report, Bureau of Business Research.
  • Kantor, P. and Boros, E. (2010) Deceptive detection methods for effective security with inadequate budgets: The testing power index. Risk Analysis: An Official Publication of the Society for Risk Analysis, 30, 663–673.
  • Kilmer, B. and Hoorens, S. (2010) Understanding illicit drug markets, supply-reduction efforts, and drug-related crime in the European Union. Technical report, RAND Corporation.
  • Kinsella, D.T. (2008) The illicit arms trade: A social network analysis, in ISA’s 49th Annual Convention, Bridging Multiple Divides. https://pdxscholar.library.pdx.edu/polisci_fac/12/.
  • Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y. and Porter, M.A. (2014) Multilayer networks. Journal of Complex Networks, 2, 203–271.
  • Kleiman, M. (2011) Surgical strikes in the drug wars: Smarter policies for both sides of the border. Foreign Affairs, 90, 89–101.
  • Koen, H., de Villiers, J.P., Roodt, H. and de Waal, A. (2017) An expert-driven causal model of the rhino poaching problem. Ecological Modelling, 347, 29–39.
  • Konrad, R.A., Trapp, A.C., Palmbach, T.M. and Blom, J.S. (2017) Overcoming human trafficking via operations research and analytics: Opportunities for methods, models, and applications. European Journal of Operational Research, 259, 733–745.
  • Kort, P.M., Feichtinger, G., Hartl, R.F. and Haunschmied, J.L. (1998) Optimal enforcement policies (crackdowns) on an illicit drug market. Optimal Control Applications and Methods, 19, 169–184.
  • Kovari, A. and Pruyt, E. (2012) Prostitution and human trafficking: A model-based exploration and policy analysis, in Proceedings of the 30th International Conference of the System Dynamics Society.
  • Krebs, V.E. (2002) Mapping networks of terrorist cells. Connections, 24, 43–52.
  • Kretschmann, L. and Münsterberg, T. (2017) Simulation-framework for illicit-goods detection in large volume freight. In Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg International Conference of Logistics, Vol. 23, epubli GmbH, Berlin, pp. 427–448.
  • Kumar, R. and Tripathi, R. (2019) Traceability of counterfeit medicine supply chain through blockchain, in 11th International Conference on Communication Systems & Networks, IEEE Press, Piscataway, NJ, pp. 568–570.
  • Lee, A.J., Nikolaev, A.G. and Jacobson, S.H. (2008) Protecting air transportation: A survey of operations research applications to aviation security. Journal of Transportation Security, 1, 160–184.
  • Levitt, S.D. and Venkatesh, S.A. (2000) An economic analysis of a drug-selling gang’s finances. Quarterly Journal of Economics, 115, 755–789.
  • Lim, M., Abdullah, A. and Jhanjhi, N. (in press) Performance optimization of criminal network hidden link prediction model with deep reinforcement learning. Journal of King Saud University - Computer and Information Sciences in Sciences.
  • Lindelauf, R., Borm, P. and Hamers, H. (2009) The influence of secrecy on the communication structure of covert networks. Social Networks, 31, 126–137.
  • Liu, K., Li, J.-A. and Lai, K.K. (2004) Single period, single product newsvendor model with random supply shock. European Journal of Operational Research, 158, 609–625.
  • Lozano, S. (2012) Information sharing in DEA: A cooperative game theory approach. European Journal of Operational Research, 222, 558–565.
  • Lü, L. and Zhou, T. (2011) Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and Its Applications, 390, 1150–1170.
  • Mackey, T.K. and Kalyanam, J. (2017) Detection of illicit online sales of fentanyls via twitter. F1000Research, 6, 1937.
  • Magliocca, N.R., McSweeney, K., Sesnie, S.E., Tellman, E., Devine, J.A., Nielsen, E.A., Pearson, Z. and Wrathall, D.J. (2019) Modeling cocaine traffickers and counterdrug interdiction forces as a complex adaptive system. Proceedings of the National Academy of Sciences of the United States of America, 116, 7784–7792.
  • Malaviya, A., Rainwater, C. and Sharkey, T. (2012) Multi-period network interdiction problems with applications to city-level drug enforcement. IIE Transactions, 44, 368–380.
  • Malliaros, F.D. and Vazirgiannis, M. (2013) Clustering and community detection in directed networks: A survey. Physics Reports, 533, 95–142.
  • Malm, A. and Bichler, G. (2011) Networks of collaborating criminals: Assessing the structural vulnerability of drug markets. Journal of Research in Crime and Delinquency, 48, 271–297.
  • Marciani, G., Porretta, M., Nardelli, M. and Italiano, G.F. (2017) A data streaming approach to link mining in criminal networks, in 5th International Conference on Future Internet of Things and Cloud Workshops., IEEE Press, Piscataway, NJ, pp. 138–143.
  • Markowski, S., Koorey, S., Hall, P. and Brauer, J. (2009) Multi-channel supply chain for illicit small arms. Defence and Peace Economics, 20, 171–191.
  • Martonosi, S.E., Ortiz, D.S. and Willis, H.H. (2007) Evaluating the viability of 100 per cent container inspection at America’s ports. In The Economic Impacts of Terrorist Attacks., Edward Elgar, Northampton, MA, pp. 218–241.
  • Mashiri, E. and Sebele-Mpofu, F.Y. (2015) Illicit trade, economic growth and the role of customs: A literature review. World Customs Journal, 9, 38–50.
  • Mazerolle, L., Soole, D. and Rombouts, S. (2007) Drug law enforcement: A review of the evaluation literature. Police Quarterly, 10, 115–153.
  • McCarthy, B., Hagan, J. and Cohen, L.E. (1998) Uncertainty, cooperation, and crime: Understanding the decision to co-offend. Social Forces, 77, 155–184.
  • McLay, L.A. and Dreiding, R. (2012) Multilevel, threshold-based policies for cargo container security screening systems. European Journal of Operational Research, 220, 522–529.
  • McMasters, A.W. and Mustin, T.M. (1970) Optimal interdiction of a supply network. Naval Research Logistics Quarterly, 17, 261–268.
  • Memon, B.R. (2012) Identifying important nodes in weighted covert networks using generalized centrality measures, in 2012 European Intelligence and Security Informatics Conference, IEEE Press, Piscataway, NJ, pp. 131–140.
  • Meneghini, C., Aziani, A. and Dugato, M. (2020) Modeling the structure and dynamics of transnational illicit networks: An application to cigarette trafficking. Applied Network Science, 5, 21.
  • Meng, N. (2013) Intelligent border patrol route optimization. Master’s Thesis, Texas A&M University-Kingsville.
  • Michael, B. (2012) Do customs trade facilitation programs help reduce customs-related corruption? International Journal of Public Administration, 35, 81–97.
  • Miltenburg, J. (2018) Supply chains for iilicit products: Case study of the global opiate production networks. Cogent Business & Management, 5, 1423871.
  • Mitchel, A. (2005) The ESRI Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics. Esri Press, Redlands, California.
  • Morselli, C. (2001) Structuring Mr. Nice: Entrepreneurial opportunities and brokerage positioning in the cannabis trade. Crime, Law and Social Change, 35, 203–244.
  • Morselli, C. (2009a) Hells Angels in springtime. Trends in Organized Crime, 12, 145–158.
  • Morselli, C. (2009b) Inside Criminal Networks. Springer- Verlag New York.
  • Morselli, C. (2010) Assessing vulnerable and strategic positions in a criminal network. Journal of Contemporary Criminal Justice, 26, 382–392.
  • Morselli, C. and Petit, K. (2007) Law-enforcement disruption of a drug importation network. Global Crime, 8, 109–130.
  • Morton, D.P., Pan, F. and Saeger, K.J. (2007) Models for nuclear smuggling interdiction. IIE Transactions, 39, 3–14.
  • Motoyama, M., McCoy, D., Levchenko, K., Savage, S. and Voelker, G.M. (2011) An analysis of underground forums, in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement, pp. 71–80.
  • Naik, A.V., Baveja, A., Batta, R. and Caulkins, J.P. (1996) Scheduling crackdowns on illicit drug markets. European Journal of Operational Research, 88, 231–250.
  • Natarajan, M. (2006) Understanding the structure of a large heroin distribution network: A quantitative analysis of qualitative data. Journal of Quantitative Criminology, 22, 171–192.
  • Naylor, R.T. (2004) Wages of crime: Black markets, Illegal Finance, and the Underworld Economy. Cornell University Press, Ithica, New York.
  • Nehme, M.V. (2009) Two-person games for stochastic network interdiction: models, methods, and complexities. Ph.D. thesis, The University of Texas at Austin.
  • Ozgul, F., Gok, M., Erdem, Z. and Ozal, Y. (2012) Detecting criminal networks: SNA models are compared to proprietary models, in 2012 IEEE International Conference on Intelligence and Security Informatics, IEEE Press, Piscataway, NJ, pp. 156–158.
  • Pan, F., Charlton, W.S. and Morton, D.P. (2003) A stochastic program for interdicting smuggled nuclear material, in Network Interdiction and Stochastic Integer Programming, Springer, Boston, MA, pp. 1–19.
  • Papachristos, A.V. and Smith, C. (2012) The small world of Al Capone: The embedded and multiplex nature of organized crime. http://dx.doi.org/https://doi.org/10.2139/ssrn.2159899.
  • Parthasarathy, S., Ruan, Y. and Satuluri, V. (2011) Community discovery in social networks: Applications, methods and emerging trends, in Social Network Data Analytics., Springer, Boston, MA, pp. 79–113.
  • Patel, N.G., Rorres, C., Joly, D.O., Brownstein, J.S., Boston, R., Levy, M.Z. and Smith, G. (2015) Quantitative methods of identifying the key nodes in the illegal wildlife trade network. Proceedings of the National Academy of Sciences of the United States of America, 112, 7948–7953.
  • Perera, S., Bell, M.G. and Bliemer, M.C. (2017) Network science approach to modelling the topology and robustness of supply chain networks: A review and perspective. Applied Network Science, 2, 33.
  • Popping, R. (2000) Computer-assisted Text Analysis. SAGE Publications, Thousand Oaks, CA.
  • Reuter, P. (2014) The mobility of drug trafficking. in Ending the Drug Wars, Technical report, London, 33–40.
  • Robins, G. (2009) Understanding individual behaviors within covert networks: The interplay of individual qualities, psychological predispositions, and network effects. Trends in Organized Crime, 12, 166–187.
  • Robinson, D. and Scogings, C. (2018) The detection of criminal groups in real-world fused data: Using the graph-mining algorithm “graphextract. Security Informatics, 7, 2.
  • Rydell, C.P., Caulkins, J.P. and Everingham, S.S. (1996) Enforcement or treatment? Modeling the relative efficacy of alternatives for controlling cocaine. Operations Research, 44, 687–695.
  • Sadeghi, S. and Seifi, A. (2019) Stochastic maximum flow network interdiction with endogenous uncertainty. International Journal of Supply and Operations Management, 6, 200–212.
  • Sam, L.Z., bin Maarof, M.A., Selamat, A. and Shamsuddin, S.M. (2007) Features extraction for illicit web pages identifications using independent component analysis, in 2007 International Conference on Intelligent and Advanced Systems., IEEE Press, Piscataway, NJ, pp. 139–144.
  • Sangkaran, T., Abdullah, A. and JhanJhi, N. (2020) Criminal network community detection using graphical analytic methods: A survey. EAI Endorsed Transactions on Energy Web, 7, e5.
  • Schneider, J.L. (2008) Reducing the illicit trade in endangered wildlife: The market reduction approach. Journal of Contemporary Criminal Justice, 24, 274–295.
  • Schroeder, J., Xu, J., Chen, H. and Chau, M. (2007) Automated criminal link analysis based on domain knowledge. Journal of the American Society for Information Science and Technology, 58, 842–855.
  • Schwartz, D.M. and Rouselle, T.D. (2009) Using social network analysis to target criminal networks. Trends in Organized Crime, 12, 188–207.
  • Seddon, T. (2014) Drug policy and global regulatory capitalism: The case of new psychoactive substances (NPS). International Journal of Drug Policy, 25, 1019–1024.
  • Shaikh, M.A. and Jiaxin, W. (2008) Network structure mining: Locating and isolating core members in covert terrorist networks. WSEAS Transactions on Information Science and Applications, 5, 1011–1020.
  • Shan, X. and Zhuang, J. (2015) Subsidizing to disrupt a terrorism supply chain—a four-player game, in R.A. Forder, (ed), OR, Defence and Security, The OR Essentials series. Palgrave Macmillan, London.
  • Sharkey, T.C., Cavdaroglu, B., Nguyen, H., Holman, J., Mitchell, J.E. and Wallace, W.A. (2015) Interdependent network restoration: On the value of information-sharing. European Journal of Operational Research, 244, 309–321.
  • Shelley, L.I. (2018) Corruption & illicit trade. Daedalus, 147, 127–143.
  • Sherman, G., Siebers, P.-O., Menachof, D. and Aickelin, U. (2012) Evaluating different cost-benefit analysis methods for port security operations, in Decision Making in Service Industries: A Practical Approach, CRC Press, Boca Raton, FL, pp. 279–303.
  • Sin, S. and Boyd, M. (2016) Searching for the nuclear silk road. In Nuclear Terrorism: Countering the Threat., Routledge, New York, NY, pp. 159–181.
  • Smith, J.C. and Song, Y. (2020) A survey of network interdiction models and algorithms. European Journal of Operational Research, 283, 797–811.
  • Soudijn, M. and Reuter, P. (2016) Cash and carry: The high cost of currency smuggling in the drug trade. Crime, Law and Social Change, 66, 271–290.
  • Staake, T., Thiesse, F. and Fleisch, E. (2009) The emergence of counterfeit trade: A literature review. European Journal of Marketing, 43, 320–349.
  • Stevenson, R.J. and Forsythe, L. (1998) The stolen goods market in New South Wales: An interview study with imprisoned burglars. Technical report, NSW Bureau of Crime Statistics and Research.
  • Suehr, S. and Vogiatzis, C. (2018) Now you see me: Identifying duplicate network personas, in 2018 European Intelligence and Security Informatics Conference, IEEE Press, Piscataway, NJ, pp. 23–30.
  • Taha, K. and Yoo, P.D. (2016) SIIMCO: A forensic investigation tool for identifying the influential members of a criminal organization. IEEE Transactions on Information Forensics and Security, 11, 811–822.
  • Taha, K. and Yoo, P.D. (2017) Using the spanning tree of a criminal network for identifying its leaders. IEEE Transactions on Information Forensics and Security, 12, 445–453.
  • Tammaro, A.M. (2014) Challenging illicit bulk cash flows: Next steps for US law enforcement. Journal of Homeland Security and Emergency Management, 11, 281–288.
  • Tezcan, B. and Maass, K.L. (2020) Human trafficking interdiction with decision dependent success. engrxiv.org/dt8fs.
  • The Economist Intelligence Unit Limited (2018) The global illicit trade environment index. http://illicittradeindex.eiu.com/.
  • Toledo, A., Carpi, L.C. and Atman, A. (2020) Diversity analysis exposes unexpected key roles in multiplex crime networks, in Complex Networks XI, Springer, Cham, pp. 371–382.
  • Tragler, G., Caulkins, J.P. and Feichtinger, G. (2001) Optimal dynamic allocation of treatment and enforcement in illicit drug control. Operations Research, 49, 352–362.
  • Transnational Alliance to Combat Illicit Trade (2019) Mapping the impact of illicit trade on the sustainable development goals. https://unctad.org/meetings/en/Contribution/DITC2020/_TRACIT/_IllicitTradeandSDGs/_fullreport/_en.pdf.
  • Triepels, R., Daniels, H. and Feelders, A. (2018) Data-driven fraud detection in international shipping. Expert Systems with Applications, 99, 193–202.
  • Tsirogiannis, C. and Tsirogiannis, C. (2016) Uncovering the missing routes: An algorithmic study on the illicit antiquities trade network, in Across Space and Time., Amsterdam University Press, Amsterdam, The Netherlands, pp. 508–515.
  • Tsvetovat, M. and Carley, K.M. (2003) Bouncing back: Recovery mechanisms of covert networks, in NAACSOS Conference, Pittsburgh, PA, Carnegie-Mellon University.
  • Turner, J. and Kelly, L. (2009) TRADE SECRETS: Intersections between diasporas and crime groups in the constitution of the human trafficking chain. British Journal of Criminology, 49, 184–201.
  • Van Der Veen, H. (2003) Taxing the drug trade: Coercive exploitation and the financing of rule. Crime, Law and Social Change, 40, 349–390.
  • Wagner, R.A. and Fischer, M.J. (1974) The string-to-string correction problem. Journal of the ACM, 21, 168–173.
  • Washburn, A. and Wood, K. (1995) Two-person zero-sum games for network interdiction. Operations Research, 43, 243–251.
  • Wasserman, S. and Faust, K. (1994) Social Network Analysis: Methods and Applications, Cambridge University Press, Cambridge, UK.
  • Watson, P. and Todeschini, C. (2007) The Medici Conspiracy: The Illicit Journey of Looted Antiquities–From Italy’s Tomb Raiders to the World’s Greatest Museum. PublicAffairs, New York, NY.
  • Watts, D.J. and Strogatz, S.H. (1998 ) Collective dynamics of 'small-world' networks. Nature, 393, 440–442.
  • WCO (2017) Illicit trade report 2017.
  • Wein, L.M., Liu, Y., Cao, Z. and Flynn, S.E. (2007) The optimal spatiotemporal deployment of radiation portal monitors can improve nuclear detection at overseas ports. Science & Global Security, 15, 211–233.
  • Williams, K.C. (2020) Parallel imports and the principle of exhaustion: The first sale rule in international commerce. Journal of Law and International Affairs Blog. https://sites.psu.edu/jlia/parallel-imports-and-the-principle-of-exhaustion-the-first-sale-rule-in-international-commerce/.
  • Williams, P. and Godson, R. (2002) Anticipating organized and transnational crime. Crime, Law and Social Change, 37, 311–355.
  • Williamson, O.E. (1975) Markets and Hierarchies. Free Press, New York.
  • Wilt, J. and Sharkey, T.C. (2019) Measuring the impact of coordination in disrupting illicit trafficking networks, in IISE Annual Conference Proceedings, pp. 767–772.
  • Wollmer, R. (1964) Removing arcs from a network. Operations Research, 12, 934–940.
  • Wood, R.K. (1993) Deterministic network interdiction. Mathematical and Computer Modelling, 17, 1–18.
  • World Economic Forum (2012) Network of global agenda councils reports 2011-2012. https://reports.weforum.org/global-agenda-council-2012/#cover.
  • Xu, J. and Chen, H. (2008) The topology of dark networks. Communications of the ACM, 51, 58–65.
  • Xu, J.J. and Chen, H. (2004) Fighting organized crimes: Using shortest-path algorithms to identify associations in criminal networks. Decision Support Systems, 38, 473–487.
  • Xu, J.J. and Chen, H. (2005) Crimenet explorer: A framework for criminal network knowledge discovery. ACM Transactions on Information Systems, 23, 201–226.
  • Yaqin, W. and Yuming, S. (2010) Classification model based on association rules in customs risk management application, in 2010 International Conference on Intelligent System Design and Engineering Application, IEEE Press, Piscataway, NJ, pp. 436–439.
  • Yen, J.Y. (1970) An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Quarterly of Applied Mathematics, 27, 526–530.
  • Zhang, J., Zhuang, J. and Behlendorf, B. (2018) Stochastic shortest path network interdiction with a case study of Arizona–Mexico border. Reliability Engineering & System Safety, 179, 62–73.
  • Zhao, F., Skums, P., Zelikovsky, A., Sevigny, E.L., Swahn, M.H., Strasser, S.M., Huang, Y. and Wu, Y. (2020) Computational approaches to detect illicit drug ads and find vendor communities within social media platforms. IEEE/ACM Transactions on Computational Biology and Bioinformatics Early Access, 1–1.
  • Zhao, M. (2019) The illicit distribution of precursor chemicals in China: A qualitative and quantitative analysis. International Journal for Crime, Justice and Social Democracy, 8, 106–120.

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