# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 5 Experimental Evaluation
### Experiments on Artificially Generated Networks  
We refer to the artificially-generated network model proposed by [PERSON] [37]. The artificially-generated network model consists of three parameters: the number of network nodes \(N\), the number of network edges \(|E|\), and the noise parameter \(\lambda\). In [PERSON]'s artificially-generated network, each network has four communities with the same number of nodes. There are two different types of nodes, \(U\) and \(V\) in the community, and each community contains the same number of \(U\) and \(V\) nodes. The number of sides \(|E|=2N\), that is, each U node is associated with 4 V nodes. \(\lambda\) is the network's noise, that is, the number of sides between different communities in the network. The size of \(\lambda\) ranges from 0 to 1. When \(\lambda\) is 0, it means that the network is full of noise. When \(\lambda\) is 1, it means that there is no noise in the network. In this experiment, an artificially-generated network with 256 nodes is selected. There are 128 U nodes and 128 V nodes in the network. There are four communities in the network. Each community contains 32 U nodes and 32 V nodes. There are 128 edges in each community. The initial value of \(\lambda\) is 0.1, increasing by 0.1 every time, increasing by nine times until the maximum value is 1. The DSNE algorithm, LOCD algorithm, and CBG&BEN algorithm are applied to the designed network, and the community detection is obtained. Table 1 shows the results of NMI evaluation. As Table 1 shows, the NMI index of the DSNE algorithm is better than the other two algorithms in most cases, which indicates that the community structure found by the DSNE algorithm is closer to the real community structure than the other two algorithms. Compared with the CBG&BEN algorithm, the average NMI of the DSNE algorithm is \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\lambda\) & **DSNE** & **CBG\&BEN** & **LOCD** \\ \hline
0.1 & 0.00 & 0.00 & 0.00 \\
0.2 & 0.01 & 0.01 & 0.00 \\
0.3 & **0.04** & 0.03 & 0.03 \\
0.4 & 0.09 & 0.07 & **0.10** \\
0.5 & **0.32** & 0.24 & 0.18 \\
0.6 & **0.71** & 0.67 & 0.22 \\
0.7 & **0.94** & 0.93 & 0.43 \\
0.8 & **0.96** & 0.90 & 0.51 \\
0.9 & **0.98** & 0.96 & 0.44 \\
1.00 & 1.00 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Normalized Mutual Information Index (NMI) evaluation of the experimental results of three methods in artificially-generated network with different indexes \(\lambda\). improved by 2.6%, compared with the LOCD algorithm, 12.5% improves the average NMI of the DSNE algorithm. In the \(\lambda\) decreasing from 0.6 to 0.4, the value of the NMI index showed a cliff-like downward trend. As Table 1 shows, when \(\lambda\) fluctuates around 0.5, the impact on the algorithm's accuracy is huge. When \(\lambda\in[0.1,0.4]\), the value of NMI does not exceed 0.1, which indicates that only less than 10% of the nodes in the community found by the algorithm correspond to the real network. Therefore, when \(\lambda\in[0.1,0.4]\), the community structures found by the three algorithms are invalid. The decreasing speed of the algorithm represents the anti-noise ability of the algorithm. We consider setting \(\lambda\in[0.5,1]\) and comparing the decreasing speed of the three algorithms under different \(\lambda\). The results are shown in Table 2. The average decreasing speed of the DSNE algorithm was 2.7% less than that of CBG&BEN and 7.3% less than that of LOCD. Therefore, the anti-noise ability of LOCD algorithm is superior to the other two algorithms. In artificially-generated networks, the number of nodes directly affects network structure information. Therefore, we consider setting \(\lambda\in[0.5,1]\) and implementing the DSNE algorithm on artificially-generated networks with 64, 128, and 256 nodes. The results are shown in Figure 7.  
Obviously, under different \(N\), with the increase of noise, that is, during the process of \(\lambda\) decreasing from 1 to 0, the decrease rate of NMI is different.