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Industrial Network Analysis Using Inter-Firm Transaction Data


  • Research Institute for Social Criticality, Pusan Nat’l University, Korea
  • School of Economics, Pukyong Nat’l University, Korea
  • Department of Computer Science, Pusan Nat’l University, Korea
  • Department of Economics, Pusan Nat’l University, Korea


Background/Objectives: Inter-firm transaction data can be used as basic information to grasp the effectiveness and robustness of country’s economic activity. Methods/Statistical analysis: In this paper, we constructed and analyzed an inter-firm network and inter-industry network using inter-firm transaction data from KED. From the result of analysis, we found that the distribution of transaction frequency between companies tends to follow the power-law distribution. This is because a large number of companies have trading connections few firms such as a conglomerate, implying that most transactions are concentrated in the major companies. And we analyzed the transaction type of each industry using the E-I index. According to the result, the companies which belong to the service-related industries tend to trade with the companies in other industries. Otherwise, the companies which belong to the motor manufacture-related industry and the electronic-related industry do a lot of trade with the companies in same industries. Especially, the network structure of these industries is hierarchized as a tree structure in comparison with other industries. Findings: From the inter-industry transaction network, we also found that the industries in Korea are largely divided into two groups: domestic service industries and export manufacturing. These two sub-networks commonly form a tree structure representing the hierarchical flow of transactions where a transaction flows from leaf nodes to root node in the inter-industry network. Application/Improvements: The General Construction industry is the root node that is located at the top in the network of domestic service industries. And the electronic and computer-related industries are the root nodes in the network of export manufacturing.


Inter-firm transaction network, Inter-industry transaction network, Social network analysis, E-I index, Hierarchical network, Tree structure

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  • Hong JP. The effect of buyer’s opportunistic behavior on the supplier’s R&D expenditure in the supplier relations. Korean Journal of Political Economy. 2011; 37(1):311–44.
  • Park JG. Impact of subcontracting on performance in Korean manufacturing: The difference between assemblers and component suppliers. Journal of Korean Economic Development. 2004; 17(1):1–17.
  • Borgatti SP, Mehra A, Brass DJ, Labianca G. Network analysis in the social sciences. Science. 2009; 323(5916):892–5.
  • Mantegna RN. Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems. 1999; 11(1):193–7.
  • Onnela JP, Chakraborti A, Kaski K, Kertesz J, Kanto A. Dynamics of market correlations: Taxonomy and portfolio analysis. Physical Review E. 2003; 68(5):1–13.
  • Kim JW, Park KN. A study on methodologies to develop an e-Industrial cluster hub system using social networks. Indian Journal of Science and Technology. 2015; 8(21):1–9.
  • Jung JH, Hong JP. A study on the network structure of the supplier-customer relations between flagship small companies. The Korean Small Business Review. 2015; 37(4):77– 103.
  • Lee YJ, Kim EK, Cho HG, Woo G. Detecting and visualizing online dispute dynamics in replying comments. Software: Practice and Experience. 2013; 43(12):1395–413.
  • Lee YJ, Cho HG, Woo G. Analysis on stock market volatility with collective human behaviors in online message board. Proceedings of IEEE CIT2014; China. 2014. p. 482–9.
  • Kim SD, Hong SH, Lee MH. Business network characteristics among Chungcheongbukdo-based top 300 enterprises: Focused on sales relationship. Journal of the Korea Contents Association. 2014; 14(9):437–49.
  • Ho HS, Kim KM, Baek WS, Lee MH. Knowledge networking analysis of Chungnam automobile parts industry based on Social Network Analysis (SNA) methods. Journal of Korea Planning Association. 2010; 45(4):183–96.
  • Freeman LC. The development of social network analysis: With an emphasis on recent events. UK: The Sage Handbook of Social Network Analysis; 2012. p. 26–39.
  • Barabasi A. The origin of bursts and heavy tails in human dynamics. Nature. 2005 May; 435(1):207–11.


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