# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 5 Experimental Evaluation
### Experiments on Real-World Bipartite Network
The more the number of communities, the more the noise interference in the merger process, the less the communities involved in the merger, the lower the merger rate, and the final number of communities is too many. DSNE algorithm is superior to CBG&BEN algorithm and LOCD algorithm in the number of communities in most datasets. Figure 8 shows the experimental results of 3 algorithms evaluated by \(EQ_{b}\) in 11 datasets. From Figure 8, we can get a conclusion: the \(EQ_{b}\) of the DSNE algorithm is better than the other two algorithms. Because the LOCD algorithm cannot effectively detect the community structure in SAC and COL datasets, LOCD modularity in these two datasets is 0. Except for CL, PCD, and CRIM datasets, the DSNE algorithm's performance is better than the other two algorithms. In the CL dataset, DSNE is slightly 0.02 less than the CBG&BEN algorithm. In the PCD dataset, DSNE is 0.11 less than the CBG&BEN algorithm, and in the CRIME dataset, DSNE is 0.01 less than the LOCD algorithm. In conclusion, the average \(EQ_{b}\) of the DSNE algorithm is 5.5% higher than that of the CBG&BEN algorithm and 33.3% higher than that of LOCD algorithm. Figure 9 shows the experimental results of 3 algorithms evaluated by \(D\) in 11 datasets. Compared with the LOCD algorithm, except for Maria, AR, and DUS datasets, the \(D\) of the DCIM algorithm is better than the LOCD algorithm. In the Maria dataset, the \(D\) of the  
Figure 8: Experimental results of three algorithms evaluated by \(EQ_{b}\) in 11 datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Number of Communities** & **SAC** & **SW** & **CLUB** & **CL** & **AR** & **DUS** & **GP** & **CRIME** & **MARIA** & **PCD** & **COL** \\ \hline DSNE & 2 & 2 & 5 & 6 & 4 & 2 & 75 & 91 & 101 & 66 & 4819 \\ CBG\&BEN & 3 & 2 & 4 & 6 & 5 & 2 & 88 & 148 & 122 & 100 & 5215 \\ LOCD & 1 & 4 & 14 & 12 & 5 & 6 & 1 & 38 & 174 & 40 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of communities obtained by the three algorithms in the eleven datasets. DSNE algorithm is 14% less than the LOCD algorithm, and in the AR dataset, the \(D\) of the DSNE algorithm is 5% less than the LOCD algorithm. In the DUS dataset, the DSNE algorithm is compared with LOCD 6% less. In SAC and GP datasets, because the LOCD algorithm could not effectively divide the community, the \(D\) of the LOCD algorithm on these two datasets is 0. Compared with the CBG&BEN algorithm, the \(D\) of DSNE is better than the CBG&BEN algorithm in all datasets. In the SW, GP, and CRIME datasets, the \(D\) of DSNE algorithm is 17%, 17%, and 12% higher than that of the CBG&BEN algorithm, respectively. In general, the DSNE algorithm's performance on \(D\) is better than that of LOCD room rate and CBG&BEN algorithm. Using \(D\) as the evaluation criterion, the average \(D\) of the DSNE algorithm is 8.6% higher than the CBG&BEN algorithm and 30.6% higher than the LOCD algorithm. Table 6 show the results of the comparison between \(D\) and \(EQ_{b}\) under different datasets using the DSNE algorithm and CBG&BEN algorithm for community detection. Since \(EQ_{b}\) can't evaluate the detected community structure properly, Both algorithms are 0 in SCBG using \(EQ_{b}\) evaluation, the \(D\) value of CBG&BEN is 0.5 more than that of DSNE because DSNE adopts a more strict strategy when conducting sub-community merger. In SW, CL, and DUS datasets, the CBG&BEN algorithm, and DSNE algorithm use \(EQ_{b}\) as the evaluation index, and the values are relatively close. Within the range of effective data accuracy, the \(EQ_{b}\) values of the two algorithms are the same. We can see from Table 6 that, when the two algorithms use \(D\) as the evaluation index, their performance is different in the above three datasets; so, when \(EQ_{b}\) as the evaluation standard appears, the same value in the dataset, we can use \(D\) to measure the merits and demerits of community detection.