## 4 Experiment and analysis
### Simulation Result Analysis
#### 4.2.2 Comparison Result Analysis between Algorithms under Different Number of Nodes  
This section mainly compares and analyzes the performance of the five algorithms mentioned above under different number of nodes. For those routing algorithms based on contextual message in opportunistic social networks, the message carrier needs to forward the message to multiple relay nodes. When the number of neighbor nodes around the message carrier is insufficient, it may not be able to select a plurality of suitable relay nodes from the neighbor nodes to perform effective data transmission. Therefore, in this experiment, we reasonably set the number of neighbor nodes as a variable to study the transmission performance of each algorithm. In addition, the comparison results show that the MSSM algorithm performs better than other algorithms in terms of delivery ratio, average end-to-end delay and overhead on average under different node numbers. As demonstrated in Table 4, the average confidence levels of delivery ratio, end-to-end delay and network overhead from the MSNN algorithm are 0.85, 0.92 and 0.95, respectively. Alternatively, the average confidence interval of delivery ratio, end-to-end delay and network overhead from the MSNN algorithm are 0.4-0.95, 0-65 and 25-200, respectively. This indicates that the probability that the real value obtained in this simulation falls in the estimated value interval is relatively large, and the experimental data obtained from this experiment are also relatively reliable. Compared with spray and wait, Epidemic, ICMT and EIMST, the improved results reflected by the experimental data are more reliable. Next, we compare the proposed approach with spray and wait, Epidemic, ICMT and EIMST in terms of delivery ratio, end-to-end delay and network overhead, respectively. Above all, we evaluated the delivery ratio of each algorithm under different number of nodes, and the comparison results of the five algorithms are shown in Figure 8. As shown in the figure, with the increase of the number of neighbor nodes from 100 to 700, the delivery ratio of most routing-forwarding algorithms shows a certain upward trend. However, the delivery ratio of Epidemic algorithm shows a downward trend when the number of nodes exceeds 300. This is because the Epidemic routing algorithm adopts information flooding mechanism to carry out information transmission, and when the number of nodes exceeds a certain range, it may cause network congestion, resulting in a lower delivery ratio. With the increase of the number of neighbor nodes from 100 to 700, we can see that the average delivery ratio of the MSSM algorithm increases by 15%, 60%, 7% and 6% as compared with those from \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Average Confidence Level** & **Average Confidence Interval** \\ \hline Delivery ratio & 0.85 & 0.4–0.95 \\ Average end-to-end delay & 0.92 & 0–65 \\ Average network overhead & 0.95 & 25–200 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average confidence interval of MSNN with the number of nodes increases 100 to 700. Figure 7: Overhead on average with various mobile similarity threshold. spray and wait, Epidemic, ICMT and EIMST, respectively. Especially, when the number of mobile nodes in communication area fluctuates from 200 to 300, the delivery ratio of MSSN algorithm is very close to those from EIMST approach. This is because both of the two algorithms are based on information exchange between a pair of nodes and community division, in which frequent information exchange between nodes in the same community is beneficial to promote the delivery ratio in the networks, and the effective strategy of message duplicates controlling is able to reduce the average end-to-end delay and network overhead in the networks. Moreover, when the number of nodes reaches 700, the delivery ratio even reaches 0.93. The MSSN algorithm comprehensively utilizes the mobile and social similarities between the neighbor nodes and the destination node.