  There is currently growing interest in modeling the information diffusion on
social networks across multi-disciplines. The majority of the corresponding
research has focused on information diffusion independently, ignoring the
network evolution in the diffusion process. Therefore, it is more reasonable to
describe the real diffusion systems by the co-evolution between network
topologies and information states. In this work, we propose a mechanism
considering the coevolution between information states and network topology
simultaneously, in which the information diffusion was executed as an SIS
process and network topology evolved based on the adaptive assumption. The
theoretical analyses based on the Markov approach were very consistent with
simulation. Both simulation results and theoretical analyses indicated that the
adaptive process, in which informed individuals would rewire the links between
the informed neighbors to a random non-neighbor node, can enhance information
diffusion (leading to much broader spreading). In addition, we obtained that
two threshold values exist for the information diffusion on adaptive networks,
i.e., if the information propagation probability is less than the first
threshold, information cannot diffuse and dies out immediately; if the
propagation probability is between the first and second threshold, information
will spread to a finite range and die out gradually; and if the propagation
probability is larger than the second threshold, information will diffuse to a
certain size of population in the network. These results may shed some light on
understanding the co-evolution between information diffusion and network
topology.
