# 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 recent years, significant progress has been made in supply chain network modeling using complex network theory. A literature search using the query TS = \"supply chain\" AND TS = \"complex network\" in the \"SCI-E\" and \"SSCI\" databases yielded 187 publications. These studies were analyzed with CiteSpace to track the evolution of research hotspots, as shown in Figure 4. Terms such as \"complex network theory\", \"resilience\", \"cascading failure\", and \"risk transmission\" have shown notable citation bursts in recent years, highlighting their growing importance in supply chain resilience research. For instance, scholars like [PERSON] have advanced supply chain network theory by applying complex network theory. Their work includes understanding the network properties of supply chains as complex adaptive systems and analyzing the robustness of supply chains through these properties [52]. Complex network theory abstracts nodes and edges within a system to describe and analyze the structure and behavior of complex systems. In supply chain networks, nodes represent various entities within the supply chain (e.g., suppliers and manufacturers), while edges denote the relationships between these entities (e.g., logistics and information flows). Many characteristics of supply chain networks can be represented using complex network theory, such as the concentration of a significant portion of supply tasks on a few core suppliers or logistics hubs, the small-world effect among enterprises, and the local clustering and hierarchical organization of nodes within the supply chain network [53; 54]. From a functional perspective, the supply chain can be divided into multiple layers, such as the supply layer, information layer, and financial layer. The supply layer primarily focuses on the flow of materials and products within the supply chain, the information layer is responsible for information transmission, communication, and decision support, while the financial layer emphasizes payment, financing, and cost control [55; 56]. These layers are interwoven, collectively forming the structural foundation of the supply chain network. Figure 4: Analysis of research hotspots evolution. (Note: This delineates the beginning year, end year, and the strength of the outbreak for keywords of complex network methods used in the supply chain. The blue line represents the overall timeline, while the red segment highlights the interval corresponding to the outbreak period). The complex network model offers unique advantages in analyzing risk transmission mechanisms across these layers. The intense citation burst of the keyword \"risk transmission\" between 2021 and 2024 highlights the growing academic focus on the diffusion of risks across different layers within supply chains. For instance, [PERSON] et al. observed that risk propagation in supply chains is influenced by issues such as information asymmetry between enterprises, leading to the rapid transmission of risks to other nodes and threatening the stability of the entire network [57]. Research advancements suggest that analyzing the interactions between supply, information, and other layers can provide deeper insights into optimizing the overall performance of supply chain networks. From a structural perspective, supply chains can be categorized into two types: hierarchical and non-hierarchical structures (Figure 5). Hierarchical structures are typically organized by layers based on different roles, such as suppliers, manufacturers, distributors, retailers, and customers. These layers collaborate, emphasizing the functions and positions of various roles within the supply chain. For example, suppliers handle raw material provision, manufacturers focus on production and processing, distributors manage inventory, and retailers oversee end-user sales.