An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks

Social media have dramatically changed the mode of information dissemination. Various models and algorithms have been developed to model information diffusion and address the influence maximization problem in complex social networks. However, it appears difficult for state-of-the-art models to interpret complex and reversible real interactive networks. In this paper, we propose a novel influence diffusion model, i.e., the Operator-Based Model (OBM), by leveraging the advantages offered from the heat diffusion based model and the agent-based model. The OBM improves the performance of simulated dissemination by considering the complex user context in the operator of the heat diffusion based model. The experiment obtains a high similarity of the OBM simulated trend to the real-world diffusion process by use of the dynamic time warping method. Furthermore, a novel influence maximization algorithm, i.e., the Global Topical Support Greedy algorithm (GTS-Greedy algorithm), is proposed corresponding to the OBM. The experimental results demonstrate its promising performance by comparing it against other classic algorithms.