## 1 Introduction
Subsequently, stochastic competitive learning has found extensive application in various fields such as label noise detection [15], overlapping community detection [16], prediction of the number of sentiment evolution communities [17], graph anomaly detection [18], image segmentation [19], and assisting the visually impaired [20]. These applications have demonstrated the feasibility, rationality, and effectiveness of applying stochastic competitive learning to the domain of complex network community detection. Despite the commendable effectiveness of the stochastic competitive learning algorithm in various fields, it has been found that the algorithm still has some shortcomings in the community detection task. Firstly, the random selection of initial positions for particles in stochastic competitive learning leads to unstable community detection outcomes. This randomness might result in an overly concentrated placement of different particles, affecting convergence speed and subsequently diminishing the quality of community detection. Secondly, the stochastic nature of the preferential walk process of particles and the uncertainty in selecting resurrection positions upon energy depletion contribute to suboptimal final results. Lastly, the constant increment in particle control ability leads to potential misjudgments in affiliating boundary nodes between communities of varying scales. The above issues make stochastic competitive learning underperform on community discovery tasks. In order to address the aforementioned issues, this paper proposes the Local Node Similarity-Integrated Stochastic Competitive Learning algorithm LNSSCL for unsupervised community detection. The algorithm first integrates the node degree and Salton metrics to determine the starting point of particle walk in the network. During the particle walk process, the wandering direction is guided by the walk rule that incorporates the local similarity of nodes. At the same time, the control ability of the particle is dynamically adjusted according to its current control range. When a particle runs out of energy, it is assigned a unique resurrection position. After the wandering is completed, the community label is adjusted through the node affiliation selection step to obtain the final community discovery result. The main objectives and contributions of this study are as follows:  
1. Determining the initial positions of particles based on node degrees and the Salton similarity index, ensuring fixed and dispersed particle placements to mitigate intense early-stage competition and subsequently accelerate convergence speed;
2. Incorporating the proposed node similarity measure to enhance the deterministic and directional aspects of particle preferential walk rules; refining the rules for selecting particle resurrection positions; introducing a node affiliation selection step to refine the final community detection results and enhance algorithm stability;
3. Dynamically adapting the increment of particle control ability according to the particle's current control range, thereby improving the effectiveness of detecting communities of varying sizes within the network;
4. The LNSSCL algorithm is experimentally compared with 12 representative algorithms on real network datasets and synthetic networks. The results demonstrate that the proposed algorithm enhances the community detection performance of stochastic competitive learning and, overall, outperforms other algorithms. The remainder of this paper is organized as follows. Section 2 presents related work. Section 3 introduces some related preliminary knowledge. Section 4 describes the details and main framework of the proposed LNSSCL algorithm. The experiments are shown and discussed in Section 5. Section 6 gives the conclusions of this paper.