Using Network Reliability to Understand International Food Trade Dynamics

  • Madhurima NathEmail author
  • Srinivasan Venkatramanan
  • Bryan Kaperick
  • Stephen Eubank
  • Madhav V. Marathe
  • Achla Marathe
  • Abhijin Adiga
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)


Understanding the structural and dynamical properties of food networks is critical for food security and social welfare. Here, we analyze international trade networks corresponding to four solanaceous crops obtained using the Food and Agricultural Organization trade database using Moore-Shannon network reliability. We present a novel approach to identify important dynamics-induced clusters of highly-connected nodes in a directed weighted network. Our analysis shows that the structure and dynamics can greatly vary across commodities. However, a consistent pattern that we observe in these commodity-specific networks is that almost all clusters that are formed are between adjacent countries in regions where liberal bilateral trade relations exist. Our analysis of networks of different years shows that intensification of trade has led to increased size of clusters, which implies that the number of countries spared from the network effects of disruption is reducing. Finally, applying this method to the aggregate network obtained by combining the four networks reveals clusters very different from those found in the constituent networks.


Network reliability Food networks Dynamics on networks Contagion clusters 



This work was supported in part by the United States Agency for International Development under the Cooperative Agreement NO. AID-OAA-L-15-00001 Feed the Future Innovation Lab for Integrated Pest Management, DTRA CNIMS Contract HDTRA1-11-D-0016-0001, NSF BIG DATA Grant IIS-1633028, NSF DIBBS Grant ACI-1443054, NIH Grant 1R01GM109718 and NSF NRT-DESE Grant DGE-154362.


  1. 1.
    Baskaran, T., Blöchl, F., Brück, T., Theis, F.J.: The Heckscher-Ohlin model and the network structure of international trade. Int. Rev. Econ. Financ. 20(2), 135–145 (2011)Google Scholar
  2. 2.
    Biondi, A., Guedes, R.N.C., Wan, F.H., Desneux, N.: Ecology, worldwide spread, and management of the invasive south american tomato pinworm, Tuta absoluta: past, present, and future. Annu. Rev. Entomol. 63, 239–258 (2018)Google Scholar
  3. 3.
    Campos, M.R., Biondi, A., Adiga, A., Guedes, R.N., Desneux, N.: From the western palaearctic region to beyond: Tuta absoluta 10 years after invading europe. J. Pest Sci. 90(3), 787–796 (2017)Google Scholar
  4. 4.
    Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E 72(2), 027,104 (2005)Google Scholar
  5. 5.
    Ercsey-Ravasz, M., Toroczkai, Z., Lakner, Z., Baranyi, J.: Complexity of the international agro-food trade network and its impact on food safety. PloS One 7(5), e37,810 (2012)Google Scholar
  6. 6.
    FAO: Production and trade. (2016)
  7. 7.
    Ghosh, R., Teng, S.H., Lerman, K., Yan, X.: The interplay between dynamics and networks: centrality, communities, and cheeger inequality. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1406–1415. ACM (2014)Google Scholar
  8. 8.
    Hernandez Nopsa, J.F., et al.: Ecological networks in stored grain: key postharvest nodes for emerging pests, pathogens, and mycotoxins. BioScience 65(10), 985–1002 (2015)Google Scholar
  9. 9.
    Hulme, P.E.: Trade, transport and trouble: managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46(1), 10–18 (2009)Google Scholar
  10. 10.
    Malliaros, F.D., Vazirgiannis, M.: Clustering and community detection in directed networks: a survey. Phys. Rep. 533(4), 95–142 (2013)Google Scholar
  11. 11.
    Moore, E.F., Shannon, C.E.: Reliable circuits using less reliable relays. J. Frankl. Inst. 262(3), 191–208 (1956)Google Scholar
  12. 12.
    Nath, M., Ren, Y., Khorramzadeh, Y., Eubank, S.: Determining whether a class of random graphs is consistent with an observed contact network. J. Theor. Biol. 440, 121–132 (2018).,
  13. 13.
    Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)Google Scholar
  14. 14.
    Reichardt, J., Bornholdt, S.: Detecting fuzzy community structures in complex networks with a Potts model. Phys. Rev. Lett. 93(21), 218,701 (2004)Google Scholar
  15. 15.
    Robinson, C., Shirazi, A., Liu, M., Dilkina, B.: Network optimization of food flows in the US. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 2190–2198. IEEE (2016)Google Scholar
  16. 16.
    Rosenzweig, C., Parry, M.L., et al.: Potential impact of climate change on world food supply. Nature 367(6459), 133–138 (1994)Google Scholar
  17. 17.
    Serrano, M.A., Boguná, M.: Topology of the world trade web. Phys. Rev. E 68(1), 015,101 (2003)Google Scholar
  18. 18.
    Suweis, S., Carr, J.A., Maritan, A., Rinaldo, A., D’Odorico, P.: Resilience and reactivity of global food security, p. 201507366 (2015)Google Scholar
  19. 19.
    Venkatramanan, S., et al.: Towards robust models of food flows and their role in invasive species spread. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 435–444. IEEE (2017)Google Scholar
  20. 20.
    Youssef, M., Khorramzadeh, Y., Eubank, S.: Network reliability: the effect of local network structure on diffusive processes. Phys. Rev. E 88(5), 052,810 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Madhurima Nath
    • 1
    Email author
  • Srinivasan Venkatramanan
    • 1
  • Bryan Kaperick
    • 1
  • Stephen Eubank
    • 2
  • Madhav V. Marathe
    • 2
  • Achla Marathe
    • 2
  • Abhijin Adiga
    • 1
  1. 1.Virginia TechBlacksburgUSA
  2. 2.University of VirginiaCharlottesvilleUSA

Personalised recommendations