Evolutionary Information Diffusion Over Heterogeneous Social Networks
A huge amount of information, created and forwarded by millions of people with various characteristics, is propagating through the online social networks every day. Understanding the mechanisms of the information diffusion over the social networks is critical to various applications including online advertisement and website management. Different from most of the existing works, we investigate the information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over the heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world datasets. The theoretical results are confirmed by simulations. We also test the theory on Twitter hashtag dataset. We observe that the derived evolutionary dynamics fit the data well and can predict the future diffusion data.