SSPCO: A new particle propagation model for community detection in complex network

Abstract

Community detection is of extraordinary significance in comprehending the structure and functions of complex networks. The particle competition algorithm is a quick and heuristic algorithm when applied to community detection. However, existing particle competition algorithms do not make full use of all information of networks and have several shortcomings such as poor robustness, weak stability, and low accuracy. In addition, it cannot be effectively applied to overlapping community detection. In this paper, a new particle propagation model with semi-supervised learning for community detection in social networks (SSPCO) is proposed. SSPCO divides the formation process of communities into the initialization phase, walking phase, restart phase, convergence phase and overlapping community detection phase. In the initialization phase, each team of labeled vertices generates a particle of this team. The domination level of each team particles at vertices and edges is also initialized in this phase. In the walking phase, the particle walks to one of the neighbors of the current vertex based on the proposed walking probability calculated by the proposed transfer acceptance probability and the proposed transfer proposal probability. In the process of particle walking, the particle has a possibility of entering the restart phase. The proposed restart probability determines whether the particle performs the restart mechanism. If the particle decides to restart, it will select a vertex for restart based on the domination level of this team particles at vertices. Otherwise, it continues to walk. After several particle walking and restart phases, the particle meets the convergence state. In the convergence phase, if all particles meet the proposed convergence condition, we will obtain community partition results based on the domination level of each team particles at the vertex. In the overlapping community detection phase, we can obtain overlapping community partition results based on the overlapping community detection mechanism. Experiment results reveal that SSPCO can improve the stability and accuracy of detecting communities. Moreover, SSPCO runs in near-linear time complexity, which allows it to be applied in large-sized networks.