The information propagation model of Weibo network based on spiking neural P systems

Abstract

Information propagation models in the Weibo network play a primary role in analyzing user behaviors, obtaining the propagation paths, determining the opinion leaders, and discovering the hot spots of public opinion. Existing research recognizes the critical role played by information propagation models from different aspects. However, few studies have investigated the specific details of information propagation in any systematic way. Spiking neural P (SNP, for short) systems are one of the most potential research carriers of information propagation by applying their concurrent structures and asynchronous firing rules. This paper proposes a simple and intuitive SNP variant, namely DWIP-SNP, for user behavior analysis in Weibo. The fundamental objects of information propagation in Weibo are represented by a similar SNP formalization. The forward, comment, delete, and other users’ behaviors in the Weibo network can be observed and proceeded more intuitively. Then, the DWIP-SNP systems are combined with time delays to indicate the dynamic information diffusion from the perspective of the Bio-computing systems. Finally, a real-world example of information propagation with Weibo data set is utilized to verify the effectiveness and feasibility of the model. The insights of the DWIP-SNP based propagation model gained from this study may be of assistance to user behavior understanding and information propagation in other complex networks.