Fig 1.
In- and out- degree distribution of Kyoto’s supplier-customer network.
Logarithmic binning of the horizontal and vertical axes is used in (a) and (b). The power law tail of the degree distribution can be observed here.
Fig 2.
Community structure of Kyoto’s supplier-customer network.
The network below the center represents Kyoto’s supplier-customer network. Here, the nodes represent a first-layer community. Of the 1,212 communities, only the largest 20 are described. The highlighted community is the community we analyzed. The nodes of the highlighted community network are firms.
Table 1.
Overview of the 1st level communities.
Fig 3.
Degree centrality of Kyoto’s supplier-customer network.
The color of the bar distinguishes the business type, with red, blue, gray, and black designating manufacturer, wholesaler, constructor, and others, respectively.
Fig 4.
Betweenness centrality of Kyoto’s supplier-customer network.
The color of the bar distinguishes the business type as with Fig 3.
Table 2.
Comparison of traditional craft industries with modern industries.
Fig 5.
Distribution of firms in each bow-tie component of Kyoto’s supplier-customer network.
“Proportion” refers to the ratio of the number of firms to the total number of firms in the largest weekly connected component in Kyoto’s supplier-customer network. The color of the bars denotes where the firms are located, with red designating Kyoto prefecture and blue the other regions.
Fig 6.
Profitability and productivity of the bow-tie components in Kyoto’s supplier-customer network.
The unit of productivity is million JPY. In terms of productivity, SCC has the highest and IN the lowest value among SCC, IN, and OUT.
Fig 7.
Distribution of firms in each bow-tie component of the selected subcommunities.
The numbers represent the number of firms. The Nishijin silk fabrics and Kyoto doll industries have a relatively small number of firms in SCC.
Fig 8.
Profitability of each bow-tie component of the selected industries.
The firms in SCC have high profitability within the consumer games and electric machinery industries.
Fig 9.
Productivity of each bow-tie component of the selected industries.
The unit of productivity is million JPY. The firms in SCC have high productivity within the consumer games and electric machinery industries.
Fig 10.
Response of the selected industries to random failure; the size of the largest connected component is plotted against (a) the percentage of nodes removed and (b) the percentage of nodes or links removed from each network.
Fig 11.
Target attack simulation (Degree centrality).
Response of the selected industries to target attack; the size of the largest connected component is plotted against (a) the percentage of links removed and (b) the percentage of nodes or links removed in order of decreasing degree centrality from each network.
Fig 12.
Target attack simulation (Betweenness centrality).
Response of the selected industries to target attack; the size of the largest connected component is plotted against (a) the percentage of links removed and (b) the percentage of nodes or links removed in order of decreasing betweenness centrality from each network.
Table 3.
Regression of Rlr for the modern industries.
Multiple R2: 0.4774, Adjusted R2: 0.4621, F-statistic: 31.26 on 9 and 308 DF, p-value: <2.2 × 10−16.
Table 4.
Regression of Dlr for the modern industries.
Multiple R2: 0.6003, Adjusted R2: 0.5886, F-statistic: 51.4 on 9 and 308 DF, p-value: <2.2 × 10−16.
Table 5.
Regression of Blr for the modern industries.
Multiple R2: 0.6447, Adjusted R2: 0.6343, F-statistic: 62.09 on 9 and 308 DF, p-value: <2.2 × 10−16.
Table 6.
Regression of profitability for the modern industries.
Multiple R2: 0.03523, Adjusted R2: 0.003801, F-statistic: 1.121 on 10 and 307 DF, p-value: 0.3458.
Table 7.
Regression of productivity for the modern industries.
Multiple R2: 0.07086, Adjusted R2: 0.04059, F-statistic: 2.341 on 10 and 307 DF, p-value: 0.0113.
Table 8.
Regression of Rlr for the traditional craft industries.
Multiple R2: 0.6253, Adjusted R2: 0.6142, F-statistic: 56.73 on 8 and 272 DF, p-value: <2.2 × 10−16.
Table 9.
Regression of Dlr for the traditional craft industries.
Multiple R2: 0.4365, Adjusted R2: 0.4177, F-statistic: 23.32 on 9 and 271 DF, p-value: < 2.2 × 10−16.
Table 10.
Regression of Blr for the traditional craft industries.
Multiple R2: 0.577, Adjusted R2: 0.563, F-statistic: 41.08 on 9 and 271 DF, p-value: < 2.2 × 10−16.
Table 11.
Regression of profitability for the traditional craft industries.
Multiple R2: 0.1374, Adjusted R2: 0.1054, F-statistic: 4.299 on 10 and 270 DF, p-value: 1.397 × 10−5.
Table 12.
Regression of productivity for the traditional craft industries.
Multiple R2: 0.0832, Adjusted R2: 0.04924, F-statistic: 2.45 on 10 and 270 DF, p-value: 0.008157.