  Social networks have become ubiquitous in our daily life, as such it has
attracted great research interests recently. A key challenge is that it is of
extremely large-scale with tremendous information flow, creating the phenomenon
of "Big Data". Under such a circumstance, understanding information diffusion
over social networks has become an important research issue. Most of the
existing works on information diffusion analysis are based on either network
structure modeling or empirical approach with dataset mining. However, the
information diffusion is also heavily influenced by network users' decisions,
actions and their socio-economic connections, which is generally ignored in
existing works. In this paper, we propose an evolutionary game theoretic
framework to model the dynamic information diffusion process in social
networks. Specifically, we analyze the framework in uniform degree and
non-uniform degree networks and derive the closed-form expressions of the
evolutionary stable network states. Moreover, the information diffusion over
two special networks, Erd\H{o}s-R\'enyi random network and the
Barab\'asi-Albert scale-free network, are also highlighted. To verify our
theoretical analysis, we conduct experiments by using both synthetic networks
and real-world Facebook network, as well as real-world information spreading
dataset of Twitter and Memetracker. Experiments shows that the proposed game
theoretic framework is effective and practical in modeling the social network
users' information forwarding behaviors.
