# Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
###### Abstract  
The aim of this study is to investigate the impact of network characteristics on supply chain network resilience (SCNR) when risk propagation occurs in supply chain networks (SCNs). The network characteristics we study here (e.g., clustering coefficient, scalar index, node type, etc.) are exceptionally important for real-life SCNs, but are not sufficiently considered in the previous research on SCNR. To this end, we first construct an SCN model with adjustable parameters for multiple network characteristics. Second, this SCN model is combined with a susceptible-infectious-susceptible model to construct an SCN risk propagation model. Third, we propose using the average (i.e., a novel SCNR metric considering node type) of the sizes of the maximum connected subgraphs (which contain all node types) over a period of time after risk propagation reaches a steady state. Fourth, the parameters of the SCN model are adjusted to generate SCNs with different network characteristics, and then the resilience of these SCNs is addressed accordingly. The simulation results mainly show the following: the larger the scalar index of an SCN is, the higher its resilience; the larger the clustering coefficient of an SCN is, the smaller its resilience; and the more uniform the distribution of node types is in an SCN, the higher its resilience. Our research work will help optimize SCNs' structure, which has important implications for society and practice. supply chain network resilience; risk propagation; network characteristic; node type +
Footnote †: journal: Applied sciences  
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and coping with disruptions [7, 8]. Scholars have explored factors affecting SCNR from different perspectives, including visibility [9], information sharing [10, 11], collaboration [12], blockchain technology [13, 14], network structure [15, 16], and so on. With the advent of the Industry 4.0 era, many scholars have also adopted the cutting-edge technology of Industry 4.0 to improve the resilience of supply chains [17, 18, 19, 20, 21, 22]. How to build a more resilient supply chain is a problem that urgently needs a solution. Studying the effect of network structure on SCNR has attracted extensive attention [15, 16, 23]. Currently, scholars mainly consider the effect of network type on network resilience [15, 24]. For example, the effects of network types on resilience for scale-free, random, and small-world networks have been extensively studied [25]. However, network structures vary greatly across different network types. The degree distribution of a random network obeys a Poisson distribution, while the degree distribution of a scale-free network conforms to a power-law distribution [26]. More importantly, real-life SCNs have very complex network characteristics and do not belong to a specific network type. Therefore, only studying the impact on network resilience in terms of network types is very limited. Fortunately, some scholars have begun to recognize this problem and have proposed focusing on network characteristics [15, 16, 25]. Network characteristics describe one aspect of network structure, and different network characteristics can be combined into arbitrary network types [15, 16]. The network characteristics that have been studied are the average degree, degree centrality, intermediary centrality, eigenvector centrality, information centrality, and communicability [15, 16]. However, there are still some SCN characteristics that have not been studied, such as scalar indices, clustering coefficients, and node types, which are present and important in real-life SCNs. Therefore, we will study the effect of the above network characteristics on SCNR. How to measure SCNR is of great importance in this field [6, 13].