## 5 Experiments and Discussions
### Experimental Results and Analysis
#### 5.4.2 Experimental Results and Analysis on Unlabeled Real Network Datasets  
On the seven unlabeled real network datasets, namely Lesmis, Jazz, Email, Netscience, Power Grid, Facebook, and PGP, the proposed LNSSCL algorithm was compared against 12 representative learning algorithms through experimental evaluation. The assessment of  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Karate** & **Dolphins** & **Polbooks** & **Football** \\ \hline Walktrap & 0.353 & 0.489 & 0.507 & 0.603 \\ Infomap & 0.401 & **0.532** & 0.521 & 0.600 \\ \hline CNM & 0.381 & 0.495 & 0.502 & 0.550 \\ Louvain & 0.415 & 0.516 & 0.526 & 0.602 \\ Leiden & 0.420 & 0.527 & **0.527** & 0.602 \\ \hline LPA & 0.375 & 0.499 & 0.481 & 0.583 \\ TS & 0.420 & 0.381 & 0.520 & 0.600 \\ GLIPA & **0.438** & 0.496 & 0.507 & 0.608 \\ SSCLPA & 0.415 & 0.525 & 0.518 & 0.601 \\ \hline Paris & 0.372 & 0.380 & 0.426 & 0.510 \\ \hline MSC & 0.418 & 0.495 & 0.519 & 0.600 \\ \hline SCL & 0.371 & 0.463 & 0.508 & 0.581 \\ \hline LNSSCL & 0.424 & **0.532** & 0.524 & **0.613** \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of modularity Q of each algorithm on labeled real network datasets. The largest Q are in bold.  
various algorithm's community detection outcomes was conducted using modularity Q, and the experimental results are shown in Table 6.  
Since the unlabeled network dataset is without real community structure, we can accomplish community delineation by identifying structural features with strong internal connections and sparse external connections. Modularity measures the strength of community structure in the network and evaluates the results of algorithmic community delineation when the dataset has no real community structure. The larger the modularity degree is, the better the quality of community detection and the stronger the connection within the community.  
As can be seen in Table 6, the LNSSCL algorithm achieves the maximum modularity on all seven unlabeled network datasets used in the experiments, outperforming other comparison algorithms. In addition, these network datasets are of different types, sizes, and sparsities, and the high modularity performance of the LNSCCL algorithm reflects the algorithm's good generalization and universality. In particular, the performance on two larger datasets, Facebook and PGP, shows that the algorithm has some scalability. For other algorithms, CNM, Louvain, and other algorithms oriented to maximize the modularity achieved high modularity on the Netscience, Power Grid, Facebook, and PGP datasets, with Leiden in particular being the most prominent, but did not show the best performance on the smaller datasets. LPA, TS, GLLPA, and SSCLPA algorithms are not as effective as the stable LNSSCL algorithm in the experiments, because some randomness in the node order of the label update and label propagation process cannot show stable and accurate performance. The MSC algorithm constructs a steady-state Markov similarity augmented matrix, which is capable of stable and efficient community delineation, and achieves high modularity in the experiments. Further, the NSSCL algorithm improves its modularity values by 6.7%, 12.7%, 6.4%, 4.2%, 10.3%, 5.3%, and 9.9% on Lesmis, Jazz, Email, Netscience, Power Grid, Facebook, and PGP datasets, respectively, as compared to the SCL algorithm. This shows that the LNSSCL algorithm improves the stability and accuracy of the original SCL algorithm for community detection to some extent.