## 4 Results
### Example Analysis  
To verify the validity and feasibility of the proposed evolutionary model, and combined with the theoretical analysis of the model in Section 4.1 and the model evaluation indicators given in Section 3.3, this section presents example verification analysis of this model based on the MATLAB R2015b platform. Based on the initial generation of SIN, the changes in the properties of the network topology are analyzed by analyzing the different values of the parameters \(p_{1},\ p_{2},\ p_{3},\ p_{4},\ \beta,M,\alpha,m\). **Background:** A space-based information network is a typical type of SIN. This paper uses a space-based information network as an example for verification. The initial number of nodes of the space-based information network is \(n\), and when time t evolves, the final number of nodes is \(N\). **Assumptions:** obeys the exponential distribution with \(\beta\), and \(\beta=1\). The probability of four events in the model is \(p_{1}=0.6,\ p_{2}=0.2,\ p_{3}=0.15,\ p_{4}=0.05\). **Situation 1:** Given the initial conditions to generate the model of SIN, analyze the network characteristics of the network model at this moment. Determining the initial value assumes that \(n=50\), \(w_{0}=1\), \(M=4\), \(m=3\), \(m_{1}=m_{2}=m_{3}=1\), \(\alpha=1\), and \(N=1000\). The distribution of node degree, node strength, and edge weights is shown in Figure 4, and the distribution of correlation of strength and degree is shown in Figure 5 below. As shown in Figure 4a-c, the corresponding strength distribution, degree distribution, and power distribution of the model all obey the power law distribution, and this is the same as the result of the theoretical analysis in the previous section. In Figure 5, network node strength \(s\) has a strong correlation with node \(k\). In this SIN model, there is a preferential connection mechanism for newly added nodes. That is, newly added equipment entities are more inclined to make matching connections with similarly functional and identical nodes, and this is the same as the result of the theoretical analysis in the previous section. Figure 4: _Cont._  
**Situation 2:** Other conditions are determined, changing the value of the local-world \(M\), and analyzing its impact on the features of the network topology. Determining the initial value assumes that \(n=30\), \(w_{0}=1\), \(m=3\), \(m_{1}=m_{2}=m_{3}=3\), \(\alpha=1\), \(N=1000\). Comparative analysis of the similarities and differences of the distribution of node degree, node strength, and correlation of strength and degree and edge weights by changing the value of local-world \(M\) is shown in Figures 6 and 7 below. Figure 4: Space information network characteristics analysis at the initial moment: (**a**) Distribution of node strength; (**b**) Distribution of node degree; (**c**) Distribution of edge weights. Figure 5: Space information network characteristics analysis at the initial moment: Distribution of the correlation of strength and Degree. As shown in Figure 6, keeping the other parameters unchanged, give the assignments \(M=30\) and \(M=5\) to observe the changes in the power law exponential distribution. From Figure 6a,b, it can be seen that in the case where the assignment of other parameters is determined, the larger the value of the local-world \(M\) is, the more nodes there are with a stronger intensity. Figure 6: Analysis of the influence of local-world \(M\) on network structure performance: (**a**) Distribution of node strength; (**b**) Distribution of node degree. From Figure 7a,b, we can see that the value of \(M\) does not affect the effectiveness of the merit selection. It does not affect the trend and the distribution law of edge weights. **Situation 3:** Other conditions are determined, where changing the new node increases the value of extra traffic \(\alpha\), and its impact on the network topology features is analyzed. Determining the initial value assumes that \(n=30,\ w_{0}=1,\ M=10,\ m=4,\ m_{1}=m_{2}=m_{3}=3,\ N=1000\).