Abstract
This chapter analyzes nationwide supplier–buyer relationship data for nearly a million firms and 4 million transactions in Japan. The production network constructed by firms through their transaction relations reflects the characteristics of economic activities in Japan. For an intuitive understanding of the network structure, we first visualize the network in three-dimensional space using a spring–electrostatic model. In this model, we replace nodes (firms) and links (transaction relations) by particles with identical charges and springs. This visualization shows that the network is highly heterogeneous, with some firms being tightly connected and forming groups, between which there are much looser connections. Such industrial communities are identified here using algorithms that maximize modularity, which measures the share of links encircled by a given partition of nodes, with reference to the expected share of intra-links for corresponding random networks with the same node partitions. Since major communities thereby detected are still very heterogeneous, the detection of communities is repeated within them. The 10 largest communities and their principal sub-communities are then characterized by areal and industry sectoral attributes of firms. In addition, how closely the sub-communities are related to each other is quantified by introducing a metric of “distance” between them. Finally, the hierarchical relationship between the communities is clarified by considering directional features of the transactions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Japan has eight regions consisting of several neighboring prefectures, with the exception of Hokkaido, which forms its own region. The total number of prefectures is 47.
References
Acemoglu, D., Carvalho, V. M., Ozdaglar, A., & Tahbaz-Salehi, A. (2012). The network origins of aggregate fluctuations. Econometrica, 80(5), 1977–2016.
Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47–97.
Allen, M. P., & Tildesley, D. J. (1987). Computer simulation of liquids. Oxford: Oxford University Press.
Arenas, A., Duch, J., Fernández, A., & Gómez, S. (2007). Size reduction of complex networks preserving modularity. New Journal of Physics, 9, 176.
Atalay, E., Hortaçsu, A., Roberts, J., & Syverson, C. (2011). Network structure of production. PNAS, 108, 5199–5202.
Barabási, A.-L. (2002). Linked: The new science of network. Cambridge: Perseus Publishing.
Barnes, J., & Hut, P. (1986). A hierarchical O(N log N) force-calculation algorithm. Nature, 324, 446–449.
Battista, G. D., Eades, P., Tamassia, R., & Tollis, I. G. (1998). Graph drawing: Algorithms for the visualization of graphs. Englewood Cliffs: Prentice Hall.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistics Mechanics, P10008.
Brandes, U., Delling, D., Gaertler, M., Görke, R., Hoefer, M., Nikoloski Z., & Wagner, D. (2008). On modularity clustering. IEEE Transactions on Knowledge and Data Engineering, 20(2), 172–188.
Buchanan, M. (2002). NEXUS: Small worlds and the groundbreaking science of networks. New York: W. W. Norton&Co.
Cainelli, G., Montresor, S., & Marzetti, G. V. (2012). Production and financial linkages in inter-firm networks: Structural variety, risk-sharing and resilience. Journal of Evolutionary Economics, 22(4), 711–734.
Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 066111.
Duch, J., & Arenas, A. (2005). Community detection in complex networks using extremal optimization. Physical Review E, 72, 027104.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 75–174.
Fortunato, S., & Barthélemy, M. (2007). Resolution limit in community detection. PNAS, 104, 36.
Frenkel, D., & Smit, B. (2002). Understanding molecular simululation: From algorithms to applications (2nd ed.). San Diego: Academic Press.
Fujiwara, Y., & Aoyama, H. (2010). Large-scale structure of a nation-wide production network. The European Physical Journal B, 77, 565–580.
Guimerà, R., & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks. Nature, 433, 895–900.
Hu, Y. (2006). Efficient, high-quality force-directed graph drawing. The Mathematica Journal, 10, 37–71.
Iino, T., & Iyetomi, H. (2011). Multiscale community analysis of a production network of firms in Japan. In J. Watada, et al. (Eds.), Intelligent decision technologies, SIST 10 (pp. 537–545). Berlin: Springer.
Iino, T., & Iyetomi, H. (2012a). Subcommunities and their mutual relationships in a transaction network. Progress of Theoretical Physics, 194, 144–157.
Iino, T., & Iyetomi, H. (2012b). Directional bias between communities of a production network in Japan. In Intelligent decision technologies, Vol. 2, SIST 16 (pp. 273–280). Berlin: Springer.
Iino, T., Kamehama, K., Iyetomi, H., Ikeda, Y., Ohnishi, T., Takayasu, H., & Takayasu, M. (2010). Community structure in a large-scale transaction network and visualization. Journal of Physics: Conference Series, 221, 012012.
Kamehama, K., Iino, T., Iyetomi, H., Ikeda, Y., Ohnishi, T., Takayasu, H., & Takayasu, M. (2010). Structure analyses of a large-scale transaction network through visualization based on molecular dynamics. Journal of Physics: Conference Series, 221, 012013.
Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100, 118703.
Luo, J., Baldwin, C. Y., Whitney, D. E., & Magee, C. L. (2012). The architecture of transaction networks: A comparative analysis of hierarchy in two sectors. Industrial and Corprate Change, 21(6), 1307–1335.
Medus, A., Acuña, G., & Dorso, C. O. (2005). Detection of community structures in networks via global optimization. Physica A, 358, 593–604.
Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256.
Newman, M. E. J. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69, 066133.
Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103, 8577–8582.
Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 026113.
Pfalzner, S. (1996). Many-body tree methods in physics. New York: Cambridge University Press.
Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74, 016110.
Watts, D. J. (1999). Small worlds: The dynamics of networks between order and randomness. Princeton: Princeton University Press.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.
Acknowledgement
This study received financial support through the Program for Promoting Methodological Innovation in Humanities and Social Sciences by Cross-Disciplinary Fusing from the Japan Society for the Promotion of Science (JSPS) and from the JSPS Grant-in-Aid for Scientific Research No. 22300080. We thank the Research Institute of Economy, Trade and Industry (RIETI) for providing us with access to the data on transactions between Japanese firms. We are grateful to Hideaki Aoyama, Yoshi Fujiwara, Yuichi Ikeda, Wataru Souma, and Hiroshi Yoshikawa for useful discussions on the subjects herein.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Japan
About this chapter
Cite this chapter
Iino, T., Iyetomi, H. (2015). Community Structure of a Large-Scale Production Network in Japan. In: Watanabe, T., Uesugi, I., Ono, A. (eds) The Economics of Interfirm Networks. Advances in Japanese Business and Economics, vol 4. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55390-8_3
Download citation
DOI: https://doi.org/10.1007/978-4-431-55390-8_3
Published:
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-55389-2
Online ISBN: 978-4-431-55390-8
eBook Packages: Business and EconomicsEconomics and Finance (R0)