# Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
## 2 Literature Review
### Supply Chain Network Risk Propagation  
The study of SCN risk propagation originated from the early research on inter-firm risk propagation [48]. With the development of economic globalization and the close business contacts between enterprises, the relationship between enterprises has gradually evolved into a complex network structure. The occurrence of risk at any node on a network will spread across the network and eventually cause serious damage to the whole network [1; 24]. If we can understand the mechanism of risk propagation on a network, we can then take measures to prevent and control SCN risks [15; 49]. How can we describe the process of risk propagation throughout SCNs? Mainstream approaches include complex network dynamics [4; 24], anyLogistik simulation optimization software [50], and agent-based agent simulation [16]. Complex network dynamics is a combination of infectious disease models and complex network theory [51]. It can better simulate the propagation process of risks, which is suitable to the research content of this paper [52; 53; 54]. Barabasi-Albert (BA) scale-free networks, small-world networks, and random networks are the most common types of networks used by scholars when studying SCN risk propagation using complex network dynamics methods [15; 16; 24]. Commonly used contagion models include SIS (susceptible-infectious-susceptible), SIRS (susceptible-infectious-recovered), SIR (susceptible-infectious-recovered), SEIR (susceptible-exposed-infectious-recovered), and SEIRS (susceptible-exposed-infectious-recovered-susceptible), among which SIS and SIR contagion models are most commonly used in the context of supply chain risk management [4; 53]. In this paper, we use a combination of scale-free networks and SIS models to simulate the propagation process of risks in SCNs. A large number of scholars have studied the mechanisms of risk propagation in SCNs. In this study, we divide the scholars' studies into two categories, i.e., the non network level and the network level. For the non-network level, several scholars have studied infection mechanisms [48; 53; 55], including infection rates, immunity rates, recovery rates, self-healing time, propagation direction, and the number of initial risk nodes, on the risk propagation process. Some scholars have considered the effects of firm-related factors [49; 56], including the degree of risk appetite, herd mentality, operational robustness and flexibility, the completeness of market information, different operational strategies, and financial factors. For the network level, scholars have mainly conducted the following studies. [PERSON] et al. [49], [PERSON] et al. [57], and [PERSON] and [PERSON] [58] modeled an SCN as a multilayer network rather than a single-layer network. [PERSON] et al. [59] considered the existence of weights for the edges in a network with different degrees of business transactions between node enterprises. [PERSON] et al. [53], [PERSON] and [PERSON] [15]; [PERSON], [PERSON], and [PERSON] [24]; and [PERSON], [PERSON], and [PERSON] [43] studied the influence of the degree of initial infection nodes, the average degree size, and the network type. However, there are a large number of network characteristics that have not been studied [15; 16]. The clustering coefficient describes the likelihood of business transactions occurring between neighboring node firms in an SCN [60]. The scalar index can be used to analyze the degree distribution of an SCN [61], and the node type affects the normal operation of an SCN [30; 31]. All of the above network characteristics exist in real-life SCNs and are important components of the structural properties of SCNs. And they have been studied by scholars from other fields [62]. However, it is not clear what effect they have on the resilience of SCNs. In this study, we construct a supply chain network model with adjustable clustering coefficients, scalar indices, and node-type ratios. Next, it is combined with the SIS model to simulate the risk propagation process on the SCN.