Modeling and Simulating of User Clustering on Network Based on Particle Swarm Optimization

Most of the current research about public opinion on the network focuses on the analysis of emergencies' spreading and early-warning process,ignores the user's main role on the procedure of opinion-spreading.In terms of this problem,we introduced the concept of "concept space",modeled and simulated the users' concept clustering process during the transmission of emergencies on the network by using particle swarm optimization algorithm.On the basis of users' clustering results,we analyzed the dynamic evolution model of network emergencies.Changing the parameter of velocity controls the convergence rate of user-clustering,and coordinates the evolution process of network emergencies,realizes the recognition of network hot events and early-warning for public opinion crisis.At last,we simulated the users' clustering behavior relatively based on basic PSO and speciation PSO(SPSO) algorithm.Simulation results show that SPSO algorithm is able to simulate the concept clustering more effectively.It is able to find multi-clute-ring center at the same time,and is good to set adaptively coping strategies for early warning of public opinion.