Online social sites have become one of the most popu- lar Web 2.0 applications in the Internet. As a new social media, the core feature of online social networks is the in- formation diffusion. We investigate the coupled dynamics of the structure and the information diffusion in the view of weak ties. Different from the recent work [25], we do not focus on the trace collection and analysis of the real data flowing in the network. Instead, inspired by [19], we propose a model for online social networks and take a closer look at the role of weak ties in the diffusion. pectation, selecting weak ties preferentially to republish cannot facilitate the information diffusion in the network, while the random selection can. Through extra analysis and experiments, we find that when α = − 1, the nodes with lower degrees are preferentially selected for repub- lishing, which will limit the scope of the distribution of re- publishing nodes in the following rounds. However, even for the random selection case, removal of the weak tie can make the coverage of the information decreases sharply, which is consistent with its special role in the structure. So we conclude that weak ties play a subtle role in the information diffusion in online social networks. On one hand, they play a role of bridges, which connect isolated communities and break through the trapping of informa- tion in local areas. On the other hand, selecting weak ties preferentially to republish cannot make the information diffuse faster in the network. For potential applications, we think that the weak ties might be of use in the control of the virus spread and the private information diffusion. We find that the phase transition found in the mo- bile communication network exists pervasively in online social networks, which means that the weak ties play a special role in the network structure. Then we propose a new model ID ( α, β ), which associates the strength of ties with the diffusion, to simulate how the information spreads in online social networks. Contrary to our ex- in the network. FIG. 5: (Color online) The dynamics of f$_{local}$ during the information propagation far away from the source. We perform each experiment 20 times and get the mean value as the final result. VI. SUMMARY FIG. 4: (Color online) The dynamics of f$_{pub}$ during the process of the diffusion. We perform the experiments for each pair of α and β 20 times and return the mean value as the final result. 0 2 4 6 8 10 12 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Hop f local ID(−1,0.01) ID(0,0.01) ID(1,0.01) (a) Facebook 0 2 4 6 8 10 12 14 16 18 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Hop f local ID(−1,0.01) ID(0,0.01) ID(1,0.01) (b) YouTube 6 0 1 2 3 4 5 6 7 x 10 4 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 T f pub ID(−1,0.01) ID(0,0.01) ID(1,0.01) (a) Facebook 0 2 4 6 8 10 12 x 10 5 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 T f pub ID(−1,0.01) ID(0,0.01) ID(1,0.01) (b) YouTube