Complex Network Approach to International Trade of Fossil Fuel
Energy has a prominent role for development of
nations. Countries which have energy resources also have strategic
power in the international trade of energy since it is essential for all
stages of production in the economy. Thus, it is important for
countries to analyze the weaknesses and strength of the system. On
the other side, international trade is one of the fields that are analyzed
as a complex network via network analysis. Complex network is one
of the tools to analyze complex systems with heterogeneous agents
and interaction between them. A complex network consists of nodes
and the interactions between these nodes. Total properties which
emerge as a result of these interactions are distinct from the sum of
small parts (more or less) in complex systems. Thus, standard
approaches to international trade are superficial to analyze these
systems. Network analysis provides a new approach to analyze
international trade as a network. In this network, countries constitute
nodes and trade relations (export or import) constitute edges. It
becomes possible to analyze international trade network in terms of
high degree indicators which are specific to complex networks such
as connectivity, clustering, assortativity/disassortativity, centrality,
etc. In this analysis, international trade of crude oil and coal which
are types of fossil fuel has been analyzed from 2005 to 2014 via
network analysis. First, it has been analyzed in terms of some
topological parameters such as density, transitivity, clustering etc.
Afterwards, fitness to Pareto distribution has been analyzed via
Kolmogorov-Smirnov test. Finally, weighted HITS algorithm has
been applied to the data as a centrality measure to determine the real
prominence of countries in these trade networks. Weighted HITS
algorithm is a strong tool to analyze the network by ranking countries
with regards to prominence of their trade partners. We have
calculated both an export centrality and an import centrality by
applying w-HITS algorithm to the data. As a result, impacts of the
trading countries have been presented in terms of high-degree
indicators.
[1] G. Fagiolo, T. Squartini and D. Garlaschelli, “Null models of economic
networks: the case of the world trade web”, Journal of Economic
Interaction and Coordination, vol.8, pp.75-107, 2013.
[2] H. An, W. Zhong, Y. Chen, H. Li and X. Gao, “Features and evolution
of international crude oil trade relationship: a trading-based network
analysis”, Energy, vol. 74, pp.254-259, 2014.
[3] W. Zhong, H. An, X. Gao and X. Sun, “The evolution of communities in
the international oil trade network”, Physica A, vol. 413, pp.45-52, 2014.
[4] W. Zhong and H. An, “The role of China in the international crude oil
trade network”, Energy Procedia, vol. 61, pp.2493-2496, 2014.
[5] S. Cheng, L. Song and X. Li, “Evolution of spatial pattern of crude oil
trade”, Studies in Sociology of Science, vol. 5, no. 1, pp. 1-7, 2014.
[6] H. Xiaoqing, A. Haizhong and Q. Hai, “Evolution of fossil energy
international trade pattern based on complex network”, Energy
Procedia, vol. 61, pp.476-479, 2014.
[7] J. Reichardt, “Introduction to complex networks”, in Structure in
Complex Networks Lecture Notes in Physics, vol 766, Springer-Verlag
Berlin Heidelberg, 2009: pp.1-11.
[8] W. Chow, “An anatomy of the world trade network”,
http://www.hkeconomy.gov.hk/en/pdf/An%20Anatomy%20of%20the%20World%20Trade%20Network%20%28July%202013%29.pdf,
(31.10.2013), pp. 1-20
[9] M. D. König and S. Battiston, “From graph theory to models of
economic networks: a tutorial” in Networks, Topology and Dynamics,
A.K.Naimzada et.al., Ed. Springer-Verlag Berlin Heidelberg, 2009, pp.
23-63.
[10] M. E. J. Newman, Networks An Introduction, Oxford University Press,
2010.
[11] A. Howell, “Network statistics and modeling the global trade economy:
exponential random graph models and latent space models: is geography
dead?”, University of California, 2012, unpublished thesis.
[12] G. Caldarelli, “Lectures in complex networks”,
http://www.ifr.ac.uk/netsci08/Download/Invited/ws1_Caldarelli.pdf
[13] P. Csermely, A. London, L. Wu, B. Uzzi, “Structure and dynamics of
core/periphery networks”, Journal of Complex Networks, vol. 1, pp.93-
123, 2013.
[14] X. F. Wang, G. Chen, “Complex networks: small-world, scale-free and
beyond”, IEEE Circuits and Systems Magazine, pp. 6-20, 2003.
[15] G. Fagiolo, J. Reyes and S. Schiavo. “The evolution of the world trade
web: a weighted-network analysis” Journal of Evolutionary Economics,
vol .20, no. 4, pp. 479-514, 2010.
[16] Jon M. Kleinberg, “Authoritative sources in a hyperlinked
environment”, Journal of the ACM, vol. 46, no. 5, pp.604-632, 1999.
[17] W. Wei and G. Liu, “Bringing order to the world trade network,” in Int.
Conf. on Economics Marketing and Management, IPEDR, vol.28, 2012,
pp. 88-92.
[18] T. Deguchi, K. Takahashi, H. Takayasu and M. Takayasu, “Hubs and
authorities in the world trade network using a weighted HITS
algorithm”, PLOSONE, vol.9, no. 7, pp. 1-16, 2014.
[19] E. Eren and S. Soyyiğit Kaya, “Network analysis of Turkey’s trade with
EU-28 with regards to BEC classification,” in 1st Annual Int. Conf. on
Social Sciences, Istanbul, 2015, pp. 39-64.
[20] International Energy Agency, Key World Energy Statistics 2014.
[21] M. Aktaş, “Türkiye’de kömür madenciliği ve enerjideki rolü”,
http://www.tki.gov.tr/Dosyalar/Dosya/YAZILI%20B%C4%B0LD%C4
%B0R%C4%B0%20METN%C4%B0.pdf, p.1-16.
[1] G. Fagiolo, T. Squartini and D. Garlaschelli, “Null models of economic
networks: the case of the world trade web”, Journal of Economic
Interaction and Coordination, vol.8, pp.75-107, 2013.
[2] H. An, W. Zhong, Y. Chen, H. Li and X. Gao, “Features and evolution
of international crude oil trade relationship: a trading-based network
analysis”, Energy, vol. 74, pp.254-259, 2014.
[3] W. Zhong, H. An, X. Gao and X. Sun, “The evolution of communities in
the international oil trade network”, Physica A, vol. 413, pp.45-52, 2014.
[4] W. Zhong and H. An, “The role of China in the international crude oil
trade network”, Energy Procedia, vol. 61, pp.2493-2496, 2014.
[5] S. Cheng, L. Song and X. Li, “Evolution of spatial pattern of crude oil
trade”, Studies in Sociology of Science, vol. 5, no. 1, pp. 1-7, 2014.
[6] H. Xiaoqing, A. Haizhong and Q. Hai, “Evolution of fossil energy
international trade pattern based on complex network”, Energy
Procedia, vol. 61, pp.476-479, 2014.
[7] J. Reichardt, “Introduction to complex networks”, in Structure in
Complex Networks Lecture Notes in Physics, vol 766, Springer-Verlag
Berlin Heidelberg, 2009: pp.1-11.
[8] W. Chow, “An anatomy of the world trade network”,
http://www.hkeconomy.gov.hk/en/pdf/An%20Anatomy%20of%20the%20World%20Trade%20Network%20%28July%202013%29.pdf,
(31.10.2013), pp. 1-20
[9] M. D. König and S. Battiston, “From graph theory to models of
economic networks: a tutorial” in Networks, Topology and Dynamics,
A.K.Naimzada et.al., Ed. Springer-Verlag Berlin Heidelberg, 2009, pp.
23-63.
[10] M. E. J. Newman, Networks An Introduction, Oxford University Press,
2010.
[11] A. Howell, “Network statistics and modeling the global trade economy:
exponential random graph models and latent space models: is geography
dead?”, University of California, 2012, unpublished thesis.
[12] G. Caldarelli, “Lectures in complex networks”,
http://www.ifr.ac.uk/netsci08/Download/Invited/ws1_Caldarelli.pdf
[13] P. Csermely, A. London, L. Wu, B. Uzzi, “Structure and dynamics of
core/periphery networks”, Journal of Complex Networks, vol. 1, pp.93-
123, 2013.
[14] X. F. Wang, G. Chen, “Complex networks: small-world, scale-free and
beyond”, IEEE Circuits and Systems Magazine, pp. 6-20, 2003.
[15] G. Fagiolo, J. Reyes and S. Schiavo. “The evolution of the world trade
web: a weighted-network analysis” Journal of Evolutionary Economics,
vol .20, no. 4, pp. 479-514, 2010.
[16] Jon M. Kleinberg, “Authoritative sources in a hyperlinked
environment”, Journal of the ACM, vol. 46, no. 5, pp.604-632, 1999.
[17] W. Wei and G. Liu, “Bringing order to the world trade network,” in Int.
Conf. on Economics Marketing and Management, IPEDR, vol.28, 2012,
pp. 88-92.
[18] T. Deguchi, K. Takahashi, H. Takayasu and M. Takayasu, “Hubs and
authorities in the world trade network using a weighted HITS
algorithm”, PLOSONE, vol.9, no. 7, pp. 1-16, 2014.
[19] E. Eren and S. Soyyiğit Kaya, “Network analysis of Turkey’s trade with
EU-28 with regards to BEC classification,” in 1st Annual Int. Conf. on
Social Sciences, Istanbul, 2015, pp. 39-64.
[20] International Energy Agency, Key World Energy Statistics 2014.
[21] M. Aktaş, “Türkiye’de kömür madenciliği ve enerjideki rolü”,
http://www.tki.gov.tr/Dosyalar/Dosya/YAZILI%20B%C4%B0LD%C4
%B0R%C4%B0%20METN%C4%B0.pdf, p.1-16.
@article{"International Journal of Business, Human and Social Sciences:71802", author = "Semanur Soyyiğit Kaya and Ercan Eren", title = "Complex Network Approach to International Trade of Fossil Fuel", abstract = "Energy has a prominent role for development of
nations. Countries which have energy resources also have strategic
power in the international trade of energy since it is essential for all
stages of production in the economy. Thus, it is important for
countries to analyze the weaknesses and strength of the system. On
the other side, international trade is one of the fields that are analyzed
as a complex network via network analysis. Complex network is one
of the tools to analyze complex systems with heterogeneous agents
and interaction between them. A complex network consists of nodes
and the interactions between these nodes. Total properties which
emerge as a result of these interactions are distinct from the sum of
small parts (more or less) in complex systems. Thus, standard
approaches to international trade are superficial to analyze these
systems. Network analysis provides a new approach to analyze
international trade as a network. In this network, countries constitute
nodes and trade relations (export or import) constitute edges. It
becomes possible to analyze international trade network in terms of
high degree indicators which are specific to complex networks such
as connectivity, clustering, assortativity/disassortativity, centrality,
etc. In this analysis, international trade of crude oil and coal which
are types of fossil fuel has been analyzed from 2005 to 2014 via
network analysis. First, it has been analyzed in terms of some
topological parameters such as density, transitivity, clustering etc.
Afterwards, fitness to Pareto distribution has been analyzed via
Kolmogorov-Smirnov test. Finally, weighted HITS algorithm has
been applied to the data as a centrality measure to determine the real
prominence of countries in these trade networks. Weighted HITS
algorithm is a strong tool to analyze the network by ranking countries
with regards to prominence of their trade partners. We have
calculated both an export centrality and an import centrality by
applying w-HITS algorithm to the data. As a result, impacts of the
trading countries have been presented in terms of high-degree
indicators.", keywords = "Complex network approach, fossil fuel, international
trade, network theory.", volume = "10", number = "1", pages = "52-9", }