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Extracting the Backbone of Global Value Chain from High-Dimensional Inter-Country Input-Output Network

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Complex Networks & Their Applications IX (COMPLEX NETWORKS 2020 2020)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 944))

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Abstract

The network backbone is a reduced but meaningful representation of dense complex network, which is helpful to understand the topological characteristics and grasp the key points of analysis. Among numerous existing extraction methods, there is no tailored one for the Inter-Country Input-Output (ICIO) network, which is almost a both directed and weighted complete multigraph even with all the self-loops that could exist. In this paper, according to the First Principle, we build the Global Industrial Value Chain Network (GIVCN) and redefine the inter-country and inter-sector propagating process of intermediate goods. Then the Global Value Chain (GVC) backbone can be effectively abstracted according to the trade-off between the conduction efficiency and velocity of intermediate goods. The network-based empirical analyses indicate that the backbone of GVC effectively reveals the trends of globally economic development under a totally new perspective, being different from the other studies of world economic and international trade.

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References

  1. Zhou, M., Wu, G., Xu, H.: Structure and formation of top networks in international trade, 2001–2010. Soc. Netw. 44(44), 9–21 (2016)

    Article  Google Scholar 

  2. Ahmed, N.K., Neville, J., Kompella, R.: Network sampling: from static to streaming graphs. ACM Trans. Knowl. Discov. Data 8(2), 7 (2014)

    Article  Google Scholar 

  3. Toivonen, H., Zhou, F., Hartikainen, A., Hinkka, A.: Network compression by node and edge mergers. In: Bisociative Knowledge Discovery, pp. 199–217. Springer (2012)

    Google Scholar 

  4. Blagus, N., Šubelj, L., Bajec, M.: Self-similar scaling of density in complex real-world networks. Phys. A Stat. Mech. Appl. 391(8), 2794–2802 (2012)

    Article  Google Scholar 

  5. Carmi, S., Havlin, S., Kirkpatrick, S., Shavitt, Y., Shir, E.: A model of internet topology using k-shell decomposition. Proc. Natl. Acad. Sci. U.S.A. 104(27), 11150–11154 (2007)

    Article  Google Scholar 

  6. Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)

    Article  Google Scholar 

  7. Siganos, G., Tauro, S.L., Faloutsos, M.: Jellyfish: a conceptual model for the AS internet topology. J. Commun. Netw. 8(3), 339–350 (2006)

    Article  Google Scholar 

  8. Chen, D.B., Lü, L.Y., Shang, M.S., Zhang, Y.C.: Identifying influential nodes in complex networks. Phys. A Stat. Mech. Appl. 391(4), 1777–1787 (2012)

    Article  Google Scholar 

  9. Lü, L.Y., Zhang, Y.C., Yeung, C.H., Zhou, T.: Leaders in social networks, the delicious case. PLoS ONE 6(6), e21202 (2011)

    Article  Google Scholar 

  10. Li, Q., Zhou, T., Lü, L.Y., Chen, D.B.: Identifying influential spreaders by weighted LeaderRank. Phys. A Stat. Mech. Appl. 404, 47–55 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Malang, K., Wang, S.H., Phaphuangwittayakul, A., Lü, Y.Y., Yuan, H.N., Zhang, X.Z.: Identifying influential nodes of global terrorism network: a comparison for skeleton network extraction. Phys. A Stat. Mech. Appl. 545, 123769 (2020)

    Article  Google Scholar 

  12. Zhang, X.H., Zhu, J.: Skeleton of weighted social network. Phys. A Stat. Mech. Appl. 392(6), 1547–1556 (2013)

    Article  Google Scholar 

  13. Zhao, S.X., Zhang, P.L., Li, J., Tan, A.M., Ye, F.Y.: Abstracting the core subnet of weighted networks based on link strengths. J. Assoc. Inf. Sci. Technol. 65(5), 984–994 (2014)

    Article  Google Scholar 

  14. Zhang, R.J., Stanley, H.E., Ye, F.Y.: Extracting h-backbone as a core structure in weighted networks. Sci. Rep. 8(1), 1–7 (2018)

    Article  Google Scholar 

  15. Cao, J., Ding, C., Shi, B.: Motif-based functional backbone extraction of complex networks. Phys. A Stat. Mech. Appl. 526, 121123 (2019)

    Article  Google Scholar 

  16. Kim, D.H., Noh, J.D., Jeong, H.: Scale-free trees: the skeletons of complex networks. Phys. Rev. E 70(4), 046126 (2004)

    Article  Google Scholar 

  17. Grady, D., Thiemann, C., Brockmann, D.: Robust classification of salient links in complex networks. Nat. Commun. 3(1), 199–202 (2011)

    Google Scholar 

  18. Zhang, X.H., Zhang, Z.C., Zhang, H., Wang, Q., Zhu, J.: Extracting the globally and locally adaptive backbone of complex networks. PLoS ONE 9(6), e100428 (2011)

    Article  Google Scholar 

  19. Serrano, M.A., Boguna, M., Vespignani, A.: Extracting the multiscale backbone of complex weighted networks. Proc. Natl. Acad. Sci. U.S.A. 106(16), 6483–6488 (2009)

    Article  Google Scholar 

  20. Radicchi, F., Ramasco, J.J., Fortunato, S.: Information filtering in complex weighted networks. Phys. Rev. E 83(4), 046101 (2011)

    Article  Google Scholar 

  21. Foti, N.J., Hughes, J.M., Rockmore, D.N.: Nonparametric sparsification of complex multiscale networks. PLoS ONE 6(2), e16431 (2011)

    Article  Google Scholar 

  22. Bu, Z., Wu, Z.A., Qian, L.Q., Cao, J., Xu, G.D.: A backbone extraction method with local search for complex weighted networks. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 85–88. IEEE (2014)

    Google Scholar 

  23. Coscia, M., Neffke, F.: Network backboning with noisy data. In: 2017 IEEE 33rd International Conference on Data Engineering, pp. 425–436. IEEE (2017)

    Google Scholar 

  24. Wang, Z., Wei, S., Yu, X., Zhu, K.: Characterizing global value chains: production length and upstreamness. National Bureau of Economic Research (2017)

    Google Scholar 

  25. Timmer, M.P., Los, B., Stehrer, R., Vries, G.J.D.: An anatomy of the global trade slowdown based on the WIOD 2016 release. GGDC Research Memorandum (2016)

    Google Scholar 

  26. Xing, L.Z., Dong, X.L., Guan, J., Qiao, X.Y.: Betweenness centrality for similarity-weight network and its application to measuring industrial sectors’ pivotability on the global value chain. Phys. A Stat. Mech. Appl. 516, 19–36 (2019)

    Article  Google Scholar 

  27. Foster, J.G., Foster, D.V., Grassberger, P., Paczusk, M.: Edge direction and the structure of networks. Proc. Natl. Acad. Sci. U.S.A. 107(24), 10815–10820 (2010)

    Article  Google Scholar 

  28. Latora, V., Marchiori, M.: Efficient behavior of small-world networks. Phys. Rev. Lett. 87(19), 198701 (2001)

    Article  Google Scholar 

Download references

Acknowledgement

The author acknowledges support from National Natural Science Foundation of China (Grant No. 71971006), Natural Science Foundation of Beijing Municipality (Grant No. 9194024), Humanities and Social Science Foundation of Ministry of Education of the People’s Republic of China (Grant No. 19YJCGJW014), Ri-Xin Talents Project of Beijing University of Technology (Grant Recipient: Lizhi Xing), Technology Plan Key Program of Beijing Municipal Education Commission (Grant No. KZ20181005010).

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Authors and Affiliations

  1. Beijing University of Technology, Beijing, 100124, China

    Lizhi Xing & Yu Han

Authors
  1. Lizhi Xing
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  2. Yu Han
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Corresponding author

Correspondence to Lizhi Xing .

Editor information

Editors and Affiliations

  1. Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa M. Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I, UFR Sciences et Techniques, Université de Bourgogne, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Complejos, Departamento de Matematicas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

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Xing, L., Han, Y. (2021). Extracting the Backbone of Global Value Chain from High-Dimensional Inter-Country Input-Output Network. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-65351-4_45

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  • DOI: https://doi.org/10.1007/978-3-030-65351-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65350-7

  • Online ISBN: 978-3-030-65351-4

  • eBook Packages: EngineeringEngineering (R0)

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