  Current social networks are of extremely large-scale generating tremendous
information flows at every moment. How information diffuse over social networks
has attracted much attention from both industry and academics. Most of the
existing works on information diffusion analysis are based on machine learning
methods focusing on social network structure analysis and empirical data
mining. However, the dynamics of information diffusion, which are heavily
influenced by network users' decisions, actions and their socio-economic
interactions, is generally ignored by most of existing works. In this paper, we
propose an evolutionary game theoretic framework to model the dynamic
information diffusion process in social networks. Specifically, we derive the
information diffusion dynamics in complete networks, uniform degree and
non-uniform degree networks, with the highlight of two special networks,
Erd\H{o}s-R\'enyi random network and the Barab\'asi-Albert scale-free network.
We find that the dynamics of information diffusion over these three kinds of
networks are scale-free and the same with each other when the network scale is
sufficiently large. To verify our theoretical analysis, we perform simulations
for the information diffusion over synthetic networks and real-world Facebook
networks. Moreover, we also conduct experiment on Twitter hashtags dataset,
which shows that the proposed game theoretic model can well fit and predict the
information diffusion over real social networks.
