Modeling and performance analysis of information diffusion under information overload in Facebook‐like social networks

摘要

Research on social networks has received remarkable attention, because an increasing number of people use social networks to broadcast information and stay connected with their friends. However, because of the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook-like social networks into account and proposes models to capture the characters such as the network, the user behaviors, and the process of information diffusion under information overload. The term type influence is introduced to characterize the information diffusion efficiency for users of a given type, which can be analyzed theoretically on the basis of the proposed models. Having noticed the inaccuracy of using type influence to estimate the information diffusion efficiency for a given user, we further introduce the term individual influence and propose a scalable approach to estimate it. We verify the accuracy of this approach by simulations and show that considering more nearby users leads to more computational costs, but more accurate results. Copyright © 2014 John Wiley & Sons, Ltd.