## 3 System Model Design
### Community Structure Contraction Strategy
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline N & 16 & Faction setting parameter \\ \(\tau\) & 0.17 & A threshold value that measures whether a node is important within a controllable range \\ \(\varepsilon\) & 0.42 & Parameter decides whether the community structure needs to shrink \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The optimal parameter value of our algorithm. ### Effect of Node Cache and Node Numbers on Routing Algorithms

The ESR algorithm is compared with the three types of classical algorithm mentioned above to verify its performance. This study focuses on the following parameters:

1. **Packet delivery ratio**: This parameter refers to the probability that a packet sent from the source successfully reaches the target within a certain time. 2. **Average end-to-end on delay**: This parameter comprehensively evaluates the delay caused by packet routing, waiting delay and transmission delay. 3. **Routing overhead**: This parameter shows the overhead between two nodes when information is transmitted. Figure 5 shows the packet delivery ratio of the ESR algorithm, EIMST algorithm, Spray and wait algorithm and the SCR algorithm when the node cache sizes and number of nodes change. Compared with the three other algorithms, the packet delivery ratio in ESR is the best. The delivery ratio in ESR is more than 0.93 when cache is 40 M. The reason is that nodes transfer information through communities in the ESR algorithm, and message transmission speed in communities is faster than between single nodes. As the process of forwarding information ends if you can find the community with the target node, we can quickly complete the data transmission. In the EIMST algorithm, nodes calculate the encounter probability in the network. This approach uses cooperative nodes when transferring information but does not establish a good management mechanism in the cache space. Thus, the packet delivery ratio (0.8 average) is lower than for ESR. Spray and wait takes a multi-copy approach, sending many copies of data to the communication area, and then not having enough cache to store messages. When there is some new information starting to be transferred, it is easy to cause the discarding of previous information, and so the packet transmission rate is low. Therefore, delivery ratio in this algorithm is low (0.4-0.48). Due to the formation of clusters in the SCR algorithm requiring a process which needs lots of nodes and takes a long time, the packet delivery ratio of SCR is lower than for ESR. Above all, the ESR scheme improves the delivery ratio over the traditional algorithms, on average. Figure 4: The influence of community division interval on packet delivery rate. Figure 6 shows the delay on average in these algorithms. As shown in Figure 6, the average delay in the spray and wait algorithm is high (more than 105), because the spray and wait algorithm is based on the policy of multiple copies, which can easily cause data redundancy and loss. The SCR algorithm analyzes the social attributes of nodes to form a local cluster. As the node cache and node number increases, the cluster process becomes better, and the transmission delay decreases. However, it is still higher than the ESR algorithm. The EIMST algorithm is better than the spray and wait algorithm, because all neighbors are selected by nodes, reducing data copies by a large number. However, the transmission delay is still large. In the ESR algorithm the average delay is 50, because data is delivered among nodes through communities. Therefore, the data can share the cache of nodes in the same community during transmission. The proposed ESR algorithm reduces transmission delay compared with the Spray and wait algorithm and the SCR algorithm, on average. Figure 7 explains the overhead on average. According to Figure 7, the overhead ratio of the spray and wait algorithm is the highest.