Abstract: Community structure is an important property of complex networks.It is profoundly significant for understanding the organizational structure and functions of complex networks.A community detection algorithm (Dynamic Distance Stochastic Competitive Learning,DDSCL) is proposed to solve the community detection problem of complex networks.DDSCL is based on dynamic distance and stochastic competitive learning.The algorithm first combines node degree values and Euclidean distances between nodes to determine the initial positions of particles in stochastic competitive learning,which will allow different particles to not compete within the same community at the beginning of the wander,speeding up the convergence of the particles.The dyna-mic distance between nodes is then combined with a dynamic distance algorithm to incorporate the dynamic distance between nodes into the particle prioritization walking process.The particle prioritization process is more directional and less random in this way.The particle travel process will also optimize the change in dynamic distance.When the particles reach a convergence state,the node is occupied by the particle that has the most control over it.Each particle in the network eventually corresponds to a community,and the community structure of the network is revealed according to the nodes occupied by each particle.DDSCL is compared in experimental tests on eight real network datasets,and it uses NMI and modularity Q -value as evaluation metrics.It’s found that DDSCL outperforms other algorithms overall.The algorithm first reduces the randomness of preferential walking of particles in stochastic competitive learning.Then DDSCL solves the problem of fragmented communities arising from dynamic distance algorithms,and improves the accuracy of community detection results.The experimental results show the proposed algorithm’s effectiveness.

Key words: Community detection, Complex network, Dynamic distance, Particle competition, Stochastic competitive learning