# Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game
## 1 Introduction
[7] advance the research on the networks loop model by researching the common structure of a network of a combat system, which consists of reconnaissance forces, a command department, attack forces, and target connected together by a reconnaissance network, command and control network, and engagement network. They provide a deeper analysis of [PERSON] efficiency evaluation model based on an adjacency matrix and indicate two defects of [PERSON] model [8]: first, a static model cannot reflect the dynamic changes of network structure during the operation; second, many nodes may belong to more than one classes, for example, fighter planes can be sensors, influencers, and even deciders, and most nodes can possibly be targets of enemy. [PERSON] [9] compares networks with different topology structures when they combat with large numbers of decentralized agents. The finc force, intelligence, networking, and C2 methodology allows the calculation of three metrics for every C4 ISR architecture. He uses the percentage of destroyed decentralized agents to evaluate the efficiencies of these works and also compares the robustness of networks with different topology structure when they are under random and deliberate attack. [PERSON] [10] put forth his research on centralization and self-synchronization of multi-agent systems, turning his interest to self-synchronization in C2 networks. Two important recent trends in military and civilian communications have been the increasing tendency to base operations around an internal network and the increasing threats to communications infrastructure. **System modeling. ** [PERSON] [11] proposed the concept of war from complex network theory and constructed the mathematical model, knowledge, and conflict of war games. [PERSON] analyzed the complexity of war, and he believed that effect-based attack methods and networks are the key to victory. [PERSON] et al. [12; 13] conducted in-depth research on the combat system using complex networks, constructed the network topology of the combat system, analyzed the command hierarchy and command mechanism of the combat system based on simulation data, and adjusted the command nodes and communications in the combat system network. By adjusting the changes in the different ratios between the command and communication nodes in the combat system network, he studied the impact on network distribution. [PERSON] [14] made further improvements on the basis of [PERSON] [15] research, abstracted weapons and equipment as nodes and connections as edges, established a combat network model, and divided the equipment into reconnaissance, C2, and influence functions according to the functions of weapons and equipment. The node abstracts the equipment system as a combat network. The combat ring is used as an index to evaluate the combat effectiveness of the weapon equipment system. [PERSON] et al. (2016) analyzed the statistical characteristics of the combat system network based on real combat games but did not provide a generalizable model and generation algorithm, which could not guide the rational construction of the network topology on fighting in the future. **Network topology. ** The current large-scale combat system modeling research based on complex network theory lacks a generation algorithm that conforms to the reality of the war, which cannot provide basis and support for further research on the dynamic evolution of combat networks and the evaluation of combat SoS in confrontation. Complex network theory uses topology as a starting point to study the structural characteristics of networks. Therefore, studying the fragility and robustness of combat networks is a hot topic for many scholars. The most common method is to describe through global and local indicators. The global indicators include several common quality assurance such as maximum connectivity, connectivity factor, load loss rate, etc.