## 6 Conclusions and Future Work  
This paper introduces a novel unsupervised community detection algorithm named LNSSCL, which incorporates node local similarity into the process of stochastic competitive learning. Firstly, the algorithm determines the starting position of particles' walks by calculating the degree value of nodes as well as the Salton similarity index. At the same time, the fusion of node similarity optimizes the particle preferential walk rule. During the particle wandering process, the particle control capacity increment is dynamically adjusted according to the control range of each particle. When a particle runs out of energy, the particle selects the node with the largest control power within its control range for resurrection. After the particle stops wandering, the nodes in the network are selected for affiliation based on the frequency of occurrence of community labels of neighboring nodes and the effectiveness score of neighboring communities, and the community detection results are finally obtained. Comparative experiments on real network datasets and synthetic networks show that the LNSSCL algorithm is effective in improving the SCL algorithm. Compared with other representative algorithms, the LNSSCL algorithm has better quality of community detection and is able to reveal a more reasonable community structure.  
Nevertheless, the LNSSCL algorithm also has some defects. Compared with the SCL algorithm, the algorithm performs multiple node local similarity calculations during the community detection process, which requires more computational cost and complexity, and may have a larger time overhead on ultra-large networks. In the selection of some hyperparameters of the algorithm, no special parameter tuning method is used; the param  
Figure 6: Parameter sensitivity analysis for \(\Delta\): (**a**) experimental results of Polbooks; (**b**) experimental results of Football; (**c**) experimental results of Jazz; (**d**) experimental results of Email.  
eters are tuned manually. Further, the networks studied in this paper are simple undirected networks and not complex networks that are closer to the real world, such as directed, weighted, or attribute. In the next step of this study, we can consider optimizing the similarity calculation to further reduce the time overhead, adopting a dynamic adaptive hyper-parameter tuning strategy instead of traditional parameter tuning. GNN is introduced to fuse attribute information and structural features to obtain node representations and calculate node representation similarity to study the community detection strategy for attribute networks.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON] and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Double-class Special Project on Cyberspace Security and Law Enforcement Technology of the Chinese People's Public Security University (2023 SYL07).  
Not applicable.  
Data Availability Statement: Data openly available in [[http://www-personal.umich.edu/~mejn/netdata/](http://www-personal.umich.edu/~mejn/netdata/)]([http://www-personal.umich.edu/~mejn/netdata/](http://www-personal.umich.edu/~mejn/netdata/)) (accessed on 1 March 2023) and [[https://deim.urv.cat/~alexandre.arenas/data/welcome.htm](https://deim.urv.cat/~alexandre.arenas/data/welcome.htm)]([https://deim.urv.cat/~alexandre.arenas/data/welcome.htm](https://deim.urv.cat/~alexandre.arenas/data/welcome.htm)) (accessed on 1 March 2023).  
The authors thank the anonymous reviewers for their insightful comments that helped improve the quality of this study.  
The authors declare no conflict of interest.