A Multi-agent System for Modelling Preference-Based Complex Influence Diffusion in Social Networks
Influence diffusion modelling, analysis and applications in the preference-aware context draw tremendous attention to both researchers and practitioners. Most contemporary studies typically model the influence-diffusion pheromone from a centralized perspective. In this paper, we model the bi-directional influence propagation in directed weighted networks in a distributed manner with the consideration of user preference by facilitating Agent-Based Modelling. In the proposed model, each individual's personalized features and the social context are modelled based on the underlying social theories, i.e. social influence and the homophily effect. In addition, the model is capable of not only producing a certain range of dynamical behaviours based on different parameter constellation but also analyzing the evolutionary trends of a social network and capturing the dynamics in the environment. Another attractive feature is the training capability of agents, which enables them to adapt the personalized features. Comparing with traditional approaches, the proposed model is more suitable for handling the complex nature of influence diffusion, and demonstrates the advantages in simulating the real-world influence diffusion. Furthermore, we propose a novel seeding algorithm for influence maximization, named Enhanced Evolution-Based Backward selection. The algorithm utilizes the advantages offered by the proposed agent-based model. The experimental results reveal that the algorithm is superior to those state-of-the-art algorithms for influence maximization.