# A Review of Supply Chain Resilience: A Network Modeling Perspective
## 3 Current Status of Supply Chain Network Modeling Research
### Supply Chain Network Modeling Based on Complex Networks
In contrast, non-hierarchical structures treat the supply chain as an integrated whole, highlighting cross-functional coordination and integration without explicit hierarchical distinctions. Relationships among nodes in non-hierarchical networks tend to be equal, making this structure suitable for distributed or decentralized supply chain networks [58; 59]. Whether the perspective is hierarchical or non-hierarchical, complex network theory can be employed to uncover the dynamic behaviors and optimization strategies within supply chain networks. The emergent trends of the keywords \"cascading failure\" and \"disruption\" in Figure 4 suggest that using such models to study the resilience and vulnerabilities of supply chain networks has become an increasingly prominent research focus. For instance, [PERSON] et al. explored computational methods to analyze supply chain network structures, visibility, and risk propagation, offering deep insights into the functions and roles of different actors within the supply chain, as well as strategies for understanding and optimizing overall network performance [60]. In the study of applying complex network theory to supply chain networks, the cascading failure model and epidemic model are among the core methods for analyzing supply chain resilience and vulnerability [62]. The cascading failure model is primarily used to simulate chain reactions triggered by the failure of a single node within a supply chain network. [PERSON] et al. [63] developed a cascading failure model based on a project subtask network to analyze the chain reactions initiated by the failure of individual tasks  
Figure 5: **(a)** Hierarchical supply chain network structures [61] and (b) non-hierarchical supply chain network structures. and their impact on the overall project. As shown in Figure 6, the author constructed a project cascading failure network model using the NetworkX library. In the experiment, the parameters \(\beta\) and \(\alpha\) were varied to analyze the impact of node load (L) and capacity (C) on the project. Specifically, \(\beta\) changes from 0.1 to 1.5, while \(\alpha\) takes on four specific values. They also proposed corresponding risk management measures. Similarly, disrupting critical nodes in a supply chain, such as core suppliers or distributors, can lead to cascading collapses across other nodes, thereby undermining the stability of the entire network. [PERSON] et al. [64] further revealed that such models not only identify the potential risks associated with specific nodes but also offer guidance for optimizing the network structure of supply chains. For example, resilience can be enhanced by adding redundant nodes or increasing the capacity of critical nodes to mitigate risks. On the other hand, epidemic models are applied to analyze the propagation characteristics of information or risks within supply chains, analogous to the spread of diseases. For instance, when an issue arises at an information node in a supply chain, it may rapidly disseminate through the information or financial layers, thereby affecting the decision-making and resource allocation of upstream and downstream nodes. [PERSON] et al. [57] investigated the influence of risk awareness and information disclosure awareness on the spread of supply chain risks based on a two-layer partially mapped SIS model, considering factors such as network topology, information disclosure consciousness, and risk awareness. Similarly, [PERSON] et al. [56] employed a two-layer SIR model that incorporates group psychology and risk preferences to analyze how these factors influence risk propagation in the presence of different early warning scenarios. As shown in Figure 7, the author constructed a risk propagation model of the supply chain based on the SIR model. The enterprises were classified into three states: Susceptible (S), Infected (I), and Recovered (R).