Community structure based on circular flow in a large-scale transaction network

• Published in: Applied network science Vol. 4; no. 1; pp. 1 - 23
• Main Authors: Kichikawa, Yuichi, Iyetomi, Hiroshi, Iino, Takashi, Inoue, Hiroyasu
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 26.10.2019; Springer Nature B.V; SpringerOpen
• Subjects: Circularity; Complexity; Computer Appl. in Social and Behavioral Sciences; Computer Science; Decomposition; Feedback loops; Gradient flow; Helmholtz-Hodge decomposition; Nodes; Production network; Simulation and Modeling; Special Issue of the 7th International Conference on Complex Networks and Their Applications; Supply chains; Visualization; Production network; Visualization; Helmholtz-Hodge decomposition
• ISSN: 2364-8228; 2364-8228
• DOI: 10.1007/s41109-019-0202-8

The objective of this study is to shed new light on the industrial flow structure embedded in microscopic supplier-buyer relations. We first construct directed networks from actual data from interfirm transaction relations in Japan; as one example, the dataset compiled by the Tokyo Shoko Research, Ltd. in 2016 contains five million links between one million firms. Then, we analyze the industrial flow structure of such a large-scale network with a special emphasis on its hierarchy and circularity. The Helmholtz-Hodge decomposition enables us to break down the flow on a directed network into two flow components: gradient flow and circular flow. The gradient flow between a pair of nodes is given by the difference of their potentials obtained by the Helmholtz-Hodge decomposition. The gradient flow runs from a node with higher potential to a node with lower potential; hence, the potential of a node shows its hierarchical position in a network. On the other hand, the circular flow component illuminates feedback loops built in a network. The potential values averaged over firms classified by the major industrial category describe hierarchical characteristics of sectors. The ordering of sectors according to the potential agrees well with the general idea of the supply chain. We also identify industrially integrated clusters of firms by applying a flow-based community detection method to the extracted circular flow network. We then find that each of the major communities is characterized by its main industry, forming a hierarchical supply chain with feedback loops by complementary industries such as transport and services.