# A Review of Supply Chain Resilience: A Network Modeling Perspective
## 4 Resilience Assessment Methods: Current Research Status
### Resilience Assessment Methods Based on Network Structural Characteristics  
In the study of supply chain resilience, evaluating resilience based on network structural characteristics has become a key research direction. This approach primarily focuses on analyzing the structural features of supply chain networks to measure their adaptive capacity and recovery efficiency when disruptions occur. The conceptual foundation of this evaluation originates from social network analysis (SNA) in the social sciences. However, in this context, it is applied to supply chain network research to investigate the specific impacts of network structure on supply chain performance. Resilience evaluation based on network structural characteristics emphasizes analyzing the connections and interactions among various entities within a supply chain. This approach aids in understanding the mechanisms by which structural relationships between supply chain partners influence corporate performance. For instance, [PERSON] et al. [80] demonstrated that structural significance and network density are critical factors affecting operational performance. Similarly, [PERSON] et al. [81] further validated that social network analysis provides more precise insights into the characteristics and patterns of supply chain collaboration networks across different markets and scales. This resilience evaluation approach offers theoretical support for studying network factors and features. It enables researchers to identify key nodes within networks, analyze connection patterns, and compare different networks using various standardized metrics. Table 2 summarizes commonly used network structural characteristics and their definitions, whose applications in the field of supply chain management are increasingly gaining attention. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Metric** & **Definition** \\ \hline \multirow{2}{*}{Diameter} & Network Diameter & The maximum shortest path length between any two nodes in the network. \\  & Average Path Length & The average number of steps along the shortest paths for all pairs of nodes. \\ \hline \multirow{3}{*}{Degree} & Node Degree & The number of links associated with a node. \\  & Network Density & Defined as the ratio of total actual links to the total potential links in the network. \\ \cline{1-1}  & Flow Complexity & Represents the average number of outgoing flows from any node. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Typical network structural metrics [82]. Traditional supply chain management typically focuses on the direct linear relationships between buyers and suppliers. However, this linear perspective fails to capture the complex dynamics required for corporate strategies and actions. From a network perspective, the relative position of nodes within a network significantly influences overall strategies and behaviors. For example, [PERSON] et al. [83] analyzed the topological structure of transportation networks to explore their adaptability in responding to disruptions, using graph theory metrics to assess the resilience levels of network structures. Although the number of empirical studies in this area remains limited, the potential applications of resilience evaluation based on network structural characteristics in supply chain management are gradually gaining recognition. [PERSON] et al. [84] analyzed inter-firm relationship data to identify key network structural metrics correlated with supply chain efficiency. [PERSON] et al. [85] proposed a data analysis framework that integrates network centrality metrics to classify and identify associated suppliers. [PERSON] et al. [86] discussed a framework that provides managers with a method to evaluate the topological characteristics of supply chain networks under various disruption scenarios. This approach allows for the early identification of network vulnerabilities and facilitates proactive management measures. [PERSON] et al.