Node Attitude Aware Information Dissemination Model Based on Evolutionary Game in Social Networks
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Node Attitude Aware Information Dissemination Model Based on Evolutionary Game in Social Networks Hongcheng Huang 1
&
Tingting Wang 1 & Min Hu 1 & Mengyuan Dong 1 & Licheng Lai 1
Accepted: 9 November 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The information dissemination in social networks is affected by many factors. However, node attitude which is an important influence factor of information dissemination in social network have not been fully considered in the previous works. Aiming at the problem of the influence of node attitude on information dissemination, this paper proposes an information propagation model based on evolutionary game. Firstly, from the individual point of view, the node’s attitude update rules are defined according to non-Bayesian social learning rules. Secondly, an inter-node game matrix based on attitude value is established. Based on the evolution analysis paradigm, an information dissemination model based on node attitude is established. The equilibrium solution of dynamic equations is replicated for both positive and negative attitudes, and the corresponding equilibrium points are stabilized. The validity of the proposed model is verified by numerical analysis and simulation experiments. They all show that the different attitudes of nodes play an important role in information dissemination. Keywords Online social networks . Information diffusion model . Evolutionary game . Node attitudes
1 Introduction With the rapid development of Internet technology, various social software, such as Sinablog, WeChat, and Twitter, are rapidly integrated into people’s daily lives, bringing great convenience to people’s lives. People can not only communicate information, but also share their daily lives, opinions and even advertising marketing on social networks. The creation of large data presents challenges as well as opportunities in many fields [1–4]. The information dissemination in online social networks is characterized by explosiveness, rapid spread, and great influence, and its dissemination law has guided significance for advertising marketing [5], information control [6], and influence maximization [7]. Therefore, it has attracted many scholars to conduct research on the laws of information dissemination [8–11]. Since the information dissemination on social networks is similar to the spread of epidemics, epidemic propagation models and its improvement models are widely used for simulating information dissemination [12–14]. The classic * Hongcheng Huang [email protected] 1
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
epidemic propagation models divide the node states in social networks into three types: the first type is susceptible nodes that have not received the message but may spread after receiving the information; the second one is the infected nodes that have received the information and is spreading it; the last type is the recovered nodes that have received
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