# Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
## 4 Numerical Simulation
### How Clustering Coefficients Affect SCNRs  
We simulated the risk propagation process on SCNs with different clustering coefficients. Figures 6 and 7 show the resilience changes with different clustering coefficients at each time step when using resilience metrics \(y\) and \(s\), respectively. The curves in different colors in the figures represent SCNs with different clustering coefficients, the horizontal coordinate is the time step, and the vertical coordinate is the SCNR at this time step.  
The curves in Figures 6 and 7 drop sharply in the initial phase. When risk propagation reaches a steady state, the relative fluctuations of the curves in the figures are smaller. In Figure 6, the relative position of the curve is highest when \(q=0.05\) and lowest when \(q=0.45\). However, in Figure 7, the relative position of the curve is lowest when \(q=0.10\) and highest  
Figure 5: The resilience of SCNs for different scalar indices when using the resilience metric \(S\).  
Figure 6: The resilience of SCNs at each time step for different clustering coefficients when using the resilience metric \(y\).  
when \(q=0.45\). In Figures 6 and 7, the relative positions of the curves corresponding to the resilience of SCNs with different clustering coefficients are reversed.  
The variations in SCNR with different clustering coefficients measured under the two metrics are shown in Figures 8 and 9. The overall trend in the curves in Figures 8 and 9 is exactly opposite. Figure 8 is a decreasing curve; when \(q=0.05\), the SCNR is a maximum of 0.0290, and when \(q=0.45\), the SCNR is a minimum of 0.0214. A rising curve is shown in Figure 9, where the SCNR has a minimum of 0.3370 when \(q=0.10\). The SCNR has a maximum of 0.3517 when \(q=0.45\). The SCN resilience decreases with the increase in the clustering coefficient when measured by resilience metric \(Y\), while the opposite is true when measured by resilience metric \(S\). It is then calculated that the \(p\)-values under the two resilience metrics are much less than 0.01, indicating that the clustering coefficient has a significant effect on SCNR.  
Figure 8: The resilience of SCNs for different clustering coefficients when using the resilience metric \(Y\).  
Figure 7: The resilience of SCNs at each time step for different clustering coefficients when using the resilience metric \(s\).  
Next, we analyze the reasons for the different results. An increase in the clustering coefficient in an SCN indicates an increase in the number of triangles in the network. The triangle structure represents the relationship between nodal firms. The larger average clustering coefficient in the network indicates a higher degree of aggregation of network nodes. Nodal firms are closely connected to each other and the transparency of network information is high. The impact of risk propagation is reduced and the number of healthy nodes in the SCN becomes more, so \(S\) is greater. The total number of nodes in the network is fixed. The average size of triangles decreases as the number of triangles in the network increases. When risk propagates, the size of the connected subgraphs decreases, the size of the maximum full-type subgraphs decreases, and the corresponding SCNR is lower.