## 4 Experiments
### Evaluations
#### 4.2.2 Results Analysis via Threshold Setting  
Besides, for four species data, eight different threshold values were selected in the experiment: 0.05, 0.055, 0.06, 0.065, 0.07, 0.075, 0.08, and 0.085, with the result illustrated in Figures 3-5. Results show that when the threshold value increases to a certain extent, the unnecessary protein filtration is greatly reduced (illustrated in Figure 3b), and the size of the protein function module (PFM) increases (illustrated in Figure 3a). However, the other four aspects of the effect including degree of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Category** & \multicolumn{4}{c}{**SSO**} & \multicolumn{4}{c}{**PSO**} \\ \hline
**Threshold** & **Fruitfly** & **Mouse** & **Scere** & **Human** & **Fruitfly** & **Mouse** & **Scere** & **Human** \\ \hline
0.05 & 4.4 & 71.2 & 171.6 & 731.6 & 5.4 & 148.6 & 205.6 & 710.2 \\
0.055 & 4 & 69.4 & 150.4 & 501.4 & 5 & 167.4 & 217.8 & 650 \\
0.06 & 4 & 75.6 & 181.6 & 451.8 & 5 & 163.2 & 217.8 & 637.8 \\
0.065 & 4 & 73.6 & 150.8 & 407.6 & 5 & 168 & 203.8 & 683.8 \\
0.07 & 4 & 107.4 & 153 & 418.8 & 5 & 146 & 204.8 & 629.6 \\
0.075 & 4 & 78 & 150.8 & 375.6 & 5 & 168 & 204.4 & 758.2 \\
0.08 & 4 & 74.6 & 167.2 & 395.2 & 5 & 166.4 & 206 & 751.2 \\
0.085 & 4 & 76.4 & 158.8 & 402 & 5 & 167.2 & 205 & 771.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average running time comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & \multicolumn{2}{c}{**Before Matching**} & \multicolumn{2}{c}{**After Matching**} \\ \cline{2-5}  & **Interaction** & **Interactor** & **GO Annotation** & **Interactor \& GO Annotation** \\ \hline Human & 8412 & 4823 & 20,201 & 3394 \\ Scere & 24,668 & 2340 & 4680 & 2325 \\ Mouse & 2498 & 2259 & 1480 & 1447 \\ Fruitfly & 680 & 607 & 3299 & 269 \\ \hline \hline \end{tabular} \({}^{1}\) GO: Gene Ontology.  
\end{table} 
Table 3: Matching statistics \({}^{1}\). In order to evaluate the efficiency and feasibility of SSO algorithm for protein module detection, we take the human dataset and conduct experiments for evaluation (based on cluster indices described in Section 2.3). The reason for the selection of human species is that the number of nodes is more than other experimental data and the data integrity is better, which can better reflect the characteristics of the algorithm. Figure 6 shows the results under the detection strategy of PSO and SSO. In the Figure 6, we find that the curves of PSO and SSO strategies are more or less intertwined. The difference between PSO and SSO is not obvious in the index of \"cohesion\", however, the curve of SSO is more stable than PSO algorithm. This indicates that the SSO-based solution outperforms the PSO-based approach in \"Separation\". Meanwhile, Table 5 indicates that the number of function module generated by SSO algorithm is a bit lower than PSO in fruitfly species. This may be because of the small number of protein nodes in fruitfly species. Table 6 shows the filtered protein number for PSO and SSO algorithms, which indicates that SSO is significantly better than those of PSO, especially when the number of protein increases. Table 7 shows the degree of polymerization in the module for both PSO and SSO algorithms. A higher value indicates higher similarity in the module. The result also reveals that the two algorithms are relatively close, however, the SSO algorithm has better stability.