functions and features of SIN is the premise and basis for studying its network topology characteristics. Based on this, the study of the dynamic evolution model of weighted local information for SIN has important theoretical value and practical significance. The topology evolution of SIN refers to the description of the nascent, extinction, and evolution of nodes and edges of SIN topology over time. It also refers to the process and mechanism of formation, update, and change of topology structure of SIN. How to combine the composition and characteristics and establish an effective model to analyze the topology structure and dynamic evolution characteristics has evolved into an important part of SIN major theory and key technology research. The main purpose of researching the evolution of SIN is to establish a dynamic model and understand the key factors affecting the topology structure, as well as to gain a deeper understanding of the topology of SIN. Of course, it is also a hot topic of research in the current period. Study of the network topology model corresponds to different stages of network development, and it is an effective means to describe the network structure. Network topology reflects the structural relationships among various network entities, and it is the first step in designing a network. It has a significant impact on the network performance and system reliability. The vocabulary of topology is developing from the mathematics and physics field, and it only considers the positional relationship between objects and ignores their status [5]. The earliest applications were in collections, geometry, and analysis, and with the development needs, this concept was gradually introduced into the network field, and the concept of network topology emerged. In order to demonstrate the representation of typical network models in different stages, we have established a model classification from three perspectives of the regular network model, random network model, and complex network model [6], as shown in Table 1 below. In summary, with the constant deepening of research, people have found that network models based on static graphs cannot meet the most requirements. On the one hand, this is because the model cannot accurately describe the real system. On the other hand, it is because the network is changing with time, that is, continuous evolution occurs over time, and the static network model cannot describe the dynamic topology changes. Therefore, it is urgently necessary to explore and excavate a new model that considers time attributes. SIN is one of the more obvious representatives of this type of demand. By studying the relevant literature, we know that most of the research on the evolution of network topology combines the specific research objects. Among them, wireless sensor networks (WSNs) [7] and online social networks (OSNs) [8] are the most common ones. For the topology evolution of WSNs, many scholars have proposed different network evolution models based on the BA model and the LW (Local-World) model [9]. Some scholars have proposed an Energy-Aware Evolution Model (EAEM) and Energy-Balanced Evolution Model (EBEM) with scale-free features based on the LW model [10]. In [11], the evolution model of DEAD (Degree, Energy and Distance) was proposed by improving the EAEM model. Based on those evolution models, paper [12] proposed an improved dynamic evolution, and the model not only included the increase of nodes, but also the deletion and compensation of links. For the topology evolution of OSNs, [13] proposed an evolution model of a complex network based on the influence of attracting factors for the deficiency of the BA model and its extension model. After analyzing the topology structure, characteristics, and evolution rules of online social networks, paper [14] introduced a dynamic weighting approach. With the development of the Internet and