# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 1 Introduction  
Many satellites are supposed to be organized as a satellite swarm to complete space tasks because the swarm intelligence will emerge with multiple autonomous satellite clustering. The members of the satellite swarm, i.e., the satellites, must communicate with each other via inter-satellite links (ISLs), which is the precondition for the satellite swarm's operations. The satellite swarm creates an inter-satellite communication network of satellite swarm (ICNS) when its members are connected by ISLs. An ICNS is a type of satellite network that treats satellites as its nodes and ISLs as its edges. Related work on the construction of a satellite network always focuses on transforming it into an optimization problem concerning ISL assignment or topology design. [PERSON] et al. [1] modeled the ISL assignment problem as the decision optimization of the ISL selection action sequence, in which reinforcement learning is suitable for obtaining the solution. [PERSON] et al. [2] transformed the ISL assignment scheduling problem in global navigation satellite systems (GNSS) into a multi-objective optimization problem and solved it using non-dominated sorting genetic algorithm II. [PERSON] et al. [3] proposed assignment algorithms of time synchronization links based on satellite layers and ranging links combining satellite selection and weighting. [PERSON] et al. [4] modeled the ISL assignment problem in GNSS as an integer programming problem and designed a rolling weight-matching method (RWM) and a rolling weight-matching method with weight enhancement (RWMWE) to solve it. [PERSON] et al. [5] used a multi-objective simulated annealing algorithm and a two-step algorithm to assign laser ISL and radio ISL, respectively. [PERSON] et al. [6] applied integer linear programming to solve the topology design problem in the context of GNSS, considering both ranging and communication performance. [PERSON] et al. [7] proposed multi-objective discretebinary particle swarm optimization to optimize the topology of optical inter-satellite links in GNSS. These studies prefer to apply intelligent optimization methods, which has produced many positive results, yet there are also some noteworthy features in these studies, such as the dependence of prior data, a large amount of calculation, and a high number of experiments. Meanwhile, a satellite network belongs to the complex network, which is well-known for its complexity of nodes, structures, and evolution [8]. Some studies exist that have researched satellite networks through the theory of complex networks. [PERSON] et al. [9] investigated the networking feasibility of quantum key distribution constellation networks. [PERSON] et al. [10] selected the super satellite nodes according to the important coefficient of the satellite node. [PERSON] et al. [11] presented a method based on a space-time evolving graph to estimate the required wavelengths in a dynamic optical satellite network. [PERSON] et al. [12] designed a framework based on topological dynamics to evaluate the structural performance of satellite networks. [PERSON] et al. [13] proposed a topological dynamics shielding method for LEO satellite networks by establishing a static virtual network. These studies employed the theory of complex networks to analyze the functions or performances of satellite networks, but they ignored the construction of these satellite networks. It is obvious that combining the construction of a satellite network and the theory of complex networks has attracted less attention, to date. In fact, a satellite network deserves to be constructed with the theory of complex networks because it is actually a type of complex network. The method to construct a satellite network using the theory of complex networks has the following three advantages:  
1. There is no limit on the type of satellites or constellations, so it is highly adaptable, which will help standardize a set of norms in the satellite network model;
2.