A framework of multilayer social networks for communication behavior with agent-based modeling

Abstract

Agent-based modeling and simulation play an important role in sociological analysis for complex phenomena. The social communication behaviors can be reproduced by interactive agents, who can behave like human beings. In this paper, we propose a simulation framework of multilayer social networks to model high-resolution interaction between agents. The agent model contains three components: social networks, a demographic-based population and a schedule-based behavior. A common description of multilayer heterogeneous networks is presented. To evaluate the performance of the proposed framework, we reproduce the transmission of influenza H1N1 in an artificial classroom, and compare the simulation results with a real outbreak of influenza H1N1 at one university in Langfang, 2009. The simulation results show high correlation between social networks and the transmission of influenza, and demonstrate that individual-based social network models can well reproduce and analyze complex interacting behavior. Furthermore, experiments under controlled conditions are carried out to analyze the sensitivity of alternative parameters. The framework is able to model high-resolution, social communications from multiple aspects.