Community Detection for Information Propagation Relying on Particle Competition

In the process of information propagation, different communities may be formed due to different opinions, interests or hobbies. However, for the application for information propagation, targeted dynamic community detection methods have not been proposed previously. In this paper, we propose a particle competition aided community detection scheme for the sake of solving the dynamic community detection for information propagation. In comparison to traditional particle competition models, the particles in our proposed model are capable of performing the operations of walking, splitting and jumping and the domination matrix of the network changes continuously. Moreover, with the aid of combining the previous particle competition experiences as well as the defined particle's walking rules, our proposed community detection scheme can automatically select and update the core nodes based on the results of previous evolution. Finally, simulation results show both the effectiveness and superiority of our proposed particle competition aided community detection model for information propagation, which may have compelling applications in the context of the spread of opinions and computer viruses, etc.