## 5 Evaluation
### Performance Analysis  
From Figure 4, it is evident that the BlockP2P-EP data propagation model, derived from the DBSCAN clustering algorithm, and DC-Gossip, based on the OPTICS clustering algorithm, demonstrates greater clustering complexity. Furthermore, as the network size expands, the clustering time increases at an accelerated rate. In contrast, DC-SoC-DB substantially reduces clustering time within the DC-SoC model by employing a lower sampling rate for core objects. When the number of nodes reaches 300, its time consumption is only 68.2% of that of OPTICS, another density-based clustering algorithm. It is important to note that these experiments are conducted on a dynamic network to simulate real-world network scale-up scenarios. The experiments involve increasing both the number of Peers and the latency range between Peers. Specifically, the interval between each new peer joining ranges from 8 to 15 ms, the interval between inactivated peers appearances ranges from 10 to 20 ms, and the interval between re-connections of inactivated peers ranges from 15 to 25 ms. Please refer to Table 3 for additional parameter settings. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Bandwidth** & \begin{tabular}{c} **Block** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Interval of** \\ **Block Sending** \\ \end{tabular} & \begin{tabular}{c} **Initial Number** \\ **of Peers** \\ \end{tabular} & \begin{tabular}{c} **Latency** \\ **Range** \\ \end{tabular} & \begin{tabular}{c} **Forwarding** \\ **Delay** \\ \end{tabular} & \begin{tabular}{c} **Block Generation** \\ **Interval** \\ \end{tabular} & \
\begin{tabular}{c} _eps_ \\ \end{tabular} &
\begin{tabular}{c} _minPts_ \\ **minPts** \\ \end{tabular} \\ \hline
5 Mbps & 1 kb & 15 ms & 100 & 1-30 ms & 0–3 ms & 15 ms & 3 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Basic parameters setup of the experiment. Figure 4: Comparison of clustering time consumption across different models. After confirming the advantages of DC-SoC in terms of clustering efficiency, it is important to assess the usability of the propagation structures it generates. Subsequent experiments will compare the performance of each propagation structure in different network scenarios, including static, dynamic, and varying network sizes. Figure 5 presents the test results in a static network. As the network state remains unchanged during the propagation process, this set of experiments enables a visual comparison of the performance advantages and disadvantages in the push phase. The analysis of Figure 4(a) clearly demonstrates that the network structure generated by the density clustering algorithm exhibits significantly faster pushing times compared to the network structure under the native Gossip protocol. Even when the Gossip protocol sets the \(f_{out}\) parameter to 10, requiring each peer to push blocks to 10 other peers, the process of pushing 50 blocks still takes approximately 10% longer than with the density structure. In practice, the \(f_{out}\) parameter is typically not set to 10 to prevent overloading individual peers within the network. Figure 4(b) illustrates that the native Gossip protocol encounters a considerable percentage of block loss during the push phase due to randomness propagation. In contrast, the density structure effectively eliminates this problem, ensuring the absence of any missed blocks. Notably, the performance of the identical density clustering algorithm closely resembles that of the resulting network structure obtained through OPTICS and DC-SoC-DB in a static network, displaying only a marginal disparity. Consequently, as depicted in Figure 4(a), the curves of the two approaches exhibit an almost identical overlap. The test results in a dynamic network are presented in Figure 6. Figure 5(a) reveals that the propagation elapsed time for both density structures remains roughly equivalent during dynamic network states, with overlapping folding trends.