Cluster Computing

, Volume 22, Supplement 3, pp 5635–5645 | Cite as

Structure Characteristics Analysis of Diesel Sales in Complex Network Method

  • Datong Zhou
  • Huajiao LiEmail author
  • Zhiping Li
  • Jinsheng Zhou
  • Donglong Lin


With the rising profits of oil companies in the refined oil sector, the optimization of the refined oil supply chain network has received more and more attention. In most supply chains (SCs), transaction relationships between suppliers and customers are commonly considered to be an extrapolation from a linear perspective. However, this traditional linear concept of an SC is egotistic and oversimplified and does not sufficiently reflect the complex and structure of supplier–customer relationships in current economic and industrial situations. But key global knowledge can be obtained from complex network characteristics analysis of the net form sales system. For two-level network like refine oil supply network, this paper proposed an integrated framework to explore its characteristics. Through various analyses of this complex network, including visual, network scale, network agglomeration, network community and geographic information analyses, we could found the characteristics of regular network node relations and regional location characteristics, as well as a strongly correlation between correlation coefficient thresholds and the network interdependence, and also moderated the correlation between SN efficiency and SN resilience. In order to testify this supply network analysis method, we conducted a real-world SN analyses based on a Chinese province diesel supply network and describe an advanced investigation of SN theory. This method enrich the SN theory, which can benefit SN management, community economics and industrial resilience. Also the basic understanding of the diesel sales network system obtained in this paper provides guidance for further research on this network structure, which can also provide a reference for regional sales supervision and resource distribution.


Diesel Sales Complex network Supply network Structure characteristics 



This research is supported by grants from the Natural Science Foundation of China (Grant No. 71173199). The authors would like to express their gratitude to An Haizhong who provided valuable suggestions.


  1. 1.
    Dyer, J.H., Hatch, N.W.: Using supplier networks to learn faster. MIT Sloan Manag. Rev. 45, 57–63 (2004)Google Scholar
  2. 2.
    Takeda, Y., Kajikawa, Y., Sakata, I., Matsushima, K.: An analysis of geographical agglomeration and modularized industrial networks in a regional cluster: a case study at Yamagata prefecture in Japan. Technovation 28, 531–539 (2008)CrossRefGoogle Scholar
  3. 3.
    Kajikawa, Y., Takeda, Y., Sakata, I., Matsushima, K.: Multiscale analysis of interfirm networks in regional clusters. Technovation 30, 168–180 (2010)CrossRefGoogle Scholar
  4. 4.
    Kajikawa, Y., Mori, J., Sakata, I.: Identifying and bridging networks in regional clusters. Technol. Forecast. Soc. 79, 252–262 (2012)CrossRefGoogle Scholar
  5. 5.
    Kim, Y., Choi, T.Y., Yan, T., Dooley, K.: Structural investigation of supply networks: a social network analysis approach. J. Oper. Manag. 29, 194–211 (2011)CrossRefGoogle Scholar
  6. 6.
    Bellamy, M.A., Ghosh, S., Hora, M.: The influence of supply network structure on firm innovation. J. Oper. Manag. 32, 357–373 (2014)CrossRefGoogle Scholar
  7. 7.
    Zhang, C., Shen, H.Z., Li, F., Yang, H.Q.: Multi-resolution density modularity of community structure discovery in complex networks. Chin. J. Theor. Phys. 14, 506–514 (2012)Google Scholar
  8. 8.
    Lv, T.Y., Xie, W.Y., Zheng, W.M., Piao, X.F.: Evaluation indexes of weighted complex network communities and analysis of their discovery algorithms. Chin. J. Theor. Phys. 21, 145–154 (2012)Google Scholar
  9. 9.
    Pauget, B., Wald, A.: Relational competence in complex temporary organizations: the case of a French hospital construction project network. Int. J. Project Manage. 31, 200–211 (2013)CrossRefGoogle Scholar
  10. 10.
    An, H.Z., Gao, X.Y., Fang, W., Huang, X., Ding, Y.H.: The role of fluctuating modes of autocorrelation incrude oil prices. Physica A 393, 382–390 (2014)CrossRefGoogle Scholar
  11. 11.
    Nelson, K.C., Brummel, R.F., Jordan, N., Manson, S.: Social networks in complex human and natural systems: the case of rotational grazing, weak ties, and eastern US dairy landscapes. Agric. Hum. Values 31, 245–259 (2014)CrossRefGoogle Scholar
  12. 12.
    Wang, J.J., Zhou, S.G., Guan, J.H.: Characteristics of real futures trading networks. Physica A 390, 398–409 (2011)CrossRefGoogle Scholar
  13. 13.
    Gao, X.Y., An, H.Z.: Study on time series bivariate correlation fluctuation based on complex network. Chin. J. Theor. Phys. 09, 535–543 (2012)Google Scholar
  14. 14.
    Gao, X.Y., An, H.Z., Liu, H.H., Ding, Y.H.: Complex network topological properties of crude oil futures and spot price linkages. Chin. J. Theor. Phys. 06, 843–852 (2011)Google Scholar
  15. 15.
    Li, H.J., Fang, W., An, H.Z.: Words analysis of online chinese news headlines about trending events: a complex network perspective. PLoS ONE 10, e0122174 (2015)CrossRefGoogle Scholar
  16. 16.
    Xu, Y., Liu, PF., Li, X., Ren, W.: Discovering the influences of complex network effects on recovering large scale multiagent systems. Sci. World J. (2014). Google Scholar
  17. 17.
    Serrano, M.A., Boguna, M.: Topology of the world trade web. Phys. Rev. E 68, 015101 (2003)CrossRefGoogle Scholar
  18. 18.
    Cheng, S.J., Song, L., Li, X.M.: Evolution of spatial pattern of crude oil trade. Stud. Sociol. Sci. 5, 1 (2014)Google Scholar
  19. 19.
    Ji, Q., Zhang, H., Fan, Y.: Identification of global oil trade patterns: an empirical research based on complex network theory. Energ. Convers. Manage. 85, 856–865 (2014)CrossRefGoogle Scholar
  20. 20.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 10, 10008 (2008)CrossRefGoogle Scholar
  21. 21.
    Namaki, A., Shirazi, A.H., Raei, R., et al.: Network analysis of a financial market based on genuine correlation and threshold method. Physica A 390, 3835–3841 (2011)CrossRefGoogle Scholar
  22. 22.
    Palla, G., Derényi, I., Farkas, I., et al.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  23. 23.
    Donges, J.F., Zou, Y., Marwan, N.: Complex networks in climate dynamics. Eur. Phys. J. Spec. Top. 174, 157–179 (2009)CrossRefGoogle Scholar
  24. 24.
    Goh, K.I., Cusick, M.E., Valle, D.: The human disease network. Proc. Natl. Acad. Sci. USA 104, 8685–8690 (2007)CrossRefGoogle Scholar
  25. 25.
    Capaldo, A., Giannoccaro, I.: How does trust affect performance in the supply chain. The moderating role of interdependence. Int. J. Prod. Econ. 166, 36–49 (2015)CrossRefGoogle Scholar
  26. 26.
    Capaldo, A., Giannoccaro, I.: Interdependence and network-level trust in supply chain networks: a computational study. Ind. Mark. Manag. 44, 180–195 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Datong Zhou
    • 1
    • 5
  • Huajiao Li
    • 2
    • 3
    • 4
    Email author
  • Zhiping Li
    • 1
  • Jinsheng Zhou
    • 2
    • 3
    • 4
  • Donglong Lin
    • 5
  1. 1.School of EnergyChina University of GeosciencesBeijingChina
  2. 2.School of Humanities and Economic ManagementChina University of GeosciencesBeijingChina
  3. 3.Key Laboratory of Carrying Capacity Assessment for Resource and EnvironmentMinistry of Land and ResourcesBeijingChina
  4. 4.China Open Lab of Talents EvaluationMinistry of Land and ResourcesBeijingChina
  5. 5.CNPC Economics and Technology Research InstituteBeijingChina

Personalised recommendations