  In this big data era, more and more social activities are digitized thereby
becoming traceable, and thus the studies of social networks attract increasing
attention from academia. It is widely believed that social networks play
important role in the process of information diffusion. However, the opposite
question, i.e., how does information diffusion process rebuild social networks,
has been largely ignored. In this paper, we propose a new framework for
understanding this reversing effect. Specifically, we first introduce a novel
information diffusion model on social networks, by considering two types of
individuals, i.e., smart and normal individuals, and two kinds of messages,
true and false messages. Since social networks consist of human individuals,
who have self-learning ability, in such a way that the trust of an individual
to one of its neighbors increases (or decreases) if this individual received a
true (or false) message from that neighbor. Based on such a simple
self-learning mechanism, we prove that a social network can indeed become
smarter, in terms of better distinguishing the true message from the false one.
Moreover, we observe the emergence of social stratification based on the new
model, i.e., the true messages initially posted by an individual closer to the
smart one can be forwarded by more others, which is enhanced by the
self-learning mechanism. We also find the crossover advantage, i.e.,
interconnection between two chain networks can make the related individuals
possessing higher social influences, i.e., their messages can be forwarded by
relatively more others. We obtained these results theoretically and validated
them by simulations, which help better understand the reciprocity between
social networks and information diffusion.
