Community structure based on circular flow in a large-scale transaction network
Community structure based on circular flow in a large-scale transaction network
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DOI：
10.1007/s41109-019-0202-8
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发表时间：
2019-10-26
影响因子：
2.2
通讯作者：
Inoue, Hiroyasu
中科院分区：
文献类型：
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作者：
Kichikawa, Yuichi;Iyetomi, Hiroshi;Inoue, Hiroyasu
本研究的目的是揭示新的光嵌入在微观制造商-买方关系的产业流结构。我们首先根据日本企业间交易关系的实际数据构建有向网络;作为一个例子，东京商工研究所2016年编制的数据集包含100万个企业之间的500万个链接。然后，我们分析了这样一个大规模网络的产业流结构，特别强调其层次性和循环性。亥姆霍兹-霍奇分解使我们能够将有向网络上的流分解为两个流分量：梯度流和循环流。一对节点之间的梯度流由Helmholtz-Hodge分解得到的它们的势的差给出。梯度流从具有较高电势的节点流向具有较低电势的节点;因此，节点的电势显示了其在网络中的分层位置。另一方面，循环流分量阐明了网络中内置的反馈回路。按主要工业类别分类的企业的平均潜在价值描述了部门的等级特征。根据势对部门进行排序与供应链的一般思想是一致的。我们还确定了工业集成集群的公司通过应用基于流的社区检测方法提取的循环流网络。然后，我们发现，每个主要社区的特点是其主要产业，形成一个层次的供应链与反馈回路的补充产业，如运输和服务。
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.