## 5 Experiments and Discussions
### Experimental Results and Analysis  
#### 5.4.1 Experimental Results and Analysis on Labeled Real Network Datasets  
On the four labeled real network datasets, namely Karate, Dolphins, Polbooks, and Football, we conducted comparative experiments between the proposed LNSSCL algorithm and 12 other representative community detection algorithms. We evaluated the community detection results of each algorithm using NMI score and modularity Q. The experimental results are shown in Tables 4 and 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Karate** & **Dolphins** & **Polbooks** & **Football** \\ \hline Walktrap & 0.504 & 0.582 & 0.543 & 0.887 \\ Infomap & 0.699 & 0.417 & 0.529 & 0.911 \\ \hline CNM & 0.692 & 0.557 & 0.531 & 0.698 \\ Louvain & 0.587 & 0.484 & 0.569 & 0.885 \\ Leiden & 0.687 & 0.581 & 0.574 & 0.890 \\ \hline LPA & 0.445 & 0.595 & 0.534 & 0.870 \\ TS & 0.710 & 0.888 & 0.550 & 0.900 \\ GLLPA & 0.753 & 0.790 & 0.580 & 0.909 \\ SSCLPA & 0.826 & 0.616 & 0.493 & 0.919 \\ \hline Paris & 0.835 & 0.780 & 0.565 & 0.831 \\ \hline MSC & 0.836 & 0.777 & 0.539 & 0.921 \\ \hline SCL & 0.821 & 0.816 & 0.552 & 0.861 \\ \hline LNSSCL & **0.848** & **0.899** & **0.610** & **0.937** \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of the NMI of each algorithm on labeled real network datasets. The largest NMI are in bold.  
As can be seen in Tables 4 and 5, the LNSSCL algorithm achieves the highest NMI values on all labeled real network datasets and has some degree of improvement over the other algorithms. Whereas, for the comparison of modularity Q, the LNSSCL algorithm does not take the optimal value on all the datasets. Though the proposed algorithm took the highest value only on Dolphins and Football datasets, the modularity scores on Karate and Polbooks datasets were close to the highest level.  
The NMI measures the similarity between the algorithm's output community segmentation results and the real online community structure. The larger the NMI, the higher the similarity between the algorithm's output community segmentation results and the real online community structure. It can be seen that the community detection results of the LNSSCL algorithm on the four labeled real network datasets of Karate, Dolphins, Polbooks, and Football are closest to the real community structure of the above networks. In addition, the LNSSCL algorithm improves its NMI values by 3.3%, 10.2%, 10.5%, and 8.8% on the Karate, Dolphins, Polbooks, and Football datasets, respectively, compared to the SCL algorithm. This demonstrates the effectiveness of a series of improvements to the SCL algorithm by the LNSSCL algorithm, which improves the stability and accuracy of the SCL algorithm.  
For other algorithms, CNM, Louvain, and Leiden algorithms seek to maximize the modularity of the whole network, which obtains good modularity scores but fails to discover the real community structure well. LPA, TS, GLIPA, and SSCLPA algorithms tend to have a certain degree of randomness in the node order of label updating and the label propagation process, making the final community. The computational complexity of Walktrap and Infomap, two randomized wandering algorithms, is relatively high and sensitive to the parameters set by themselves. Due to the small size of the dataset used in the experiments and the existence of small communities, although they have higher modularity scores, they failed to achieve higher NMI scores, and the consistency with the real community structure is insufficient.  
Overall, the LNSSCL algorithm works best for community detection on the Karate, Dolphins, Polbooks, and Football datasets.