Abstract
Purpose With the recent development of science and technology, research on information diffusion has become increasingly important. Design/methodology/approach To analyze the process of information diffusion, researchers have proposed a framework with graphical evolutionary game theory (EGT) according to the theory of biological evolution. Findings Through this method, one can study and even predict information diffusion. Originality/value This paper summarizes three existing works using graphical EGT to discuss how to obtain the static state and the dynamics of information diffusion in social network.
Highlights
Over the past several decades, accompanied by the rapid development of the internet and mobile communications, social networks are becoming increasingly popular among numerous individuals
Works about information diffusion focused on the dynamics of information diffusion from both the
We summarized the key results of studies (Jiang et al, 2014a, 2014b) and (Cao et al, 2016) to illustrate how the graphical evolutionary game theory (EGT) can be used to model information diffusion
Summary
Over the past several decades, accompanied by the rapid development of the internet and mobile communications, social networks are becoming increasingly popular among numerous individuals. Á unn ; where : b1 1⁄4 uff þ ðk À 2Þufn À ðk À 1Þunn: Theorem 2: The population dynamics of information diffusion over non-uniform degree networks under the BD strategy update rule and weak selection scenario can be described as follows: p_ f aðkÀ. Theorem 3: In an N-users social network which can be characterized by a graph with uniform degree k, if each user updates his/her information forward strategy using the IM update rules, the evolutionary stable network states can be summarized as follows: p*f. Theorem 4: In an N-users social network which can be characterized by a graph with degree distribution l ðkÞ, if each user updates his/her information forward strategy using the BD update rule, the evolutionary stable network states can be summarized as follows:. Theorem 5: (Evolutionary dynamics): In the unknown user type model, the evolutionary dynamics for the network states pf ðiÞ and pf are given in the equations as follows: p_ f ðiÞ
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