Information diffusion model in modular microblogging networks

We focus on information diffusion in microblogging networks. As a classic problem of human dynamics in social networks, many researches have been done on information spreading in social media. In this work, we presented a new information diffusion model considering both network topology and spreading mechanism in microblogging networks. Extensive experiments have been done to estimate the impacts of reciprocity and modulrity on the outbreak size and spreading speed of a story in many artificial networks. By analyzing the experimental results in detail, we show that both reciprocity and modularity will impact the outbreak size and the spreading speed to some extent. Furthermore, modularity will impact the outbreak size much more than reciprocity. In most circumstances, the outbreak size of a story in the network with moderate modularity could be larger than that with low or high modularity. In addition, with the growth of reciprocity, the outbreak size will increase slightly. As a significant application, we have found some efficient strategies to control information spreading in real-world microblogging networks, and a better outcome will be attained to improve or restrain the outbreak size by adding or removing the edges between modules, rather than the edges with large betweenness. These findings introduced in this paper could be useful for decision-making processes in microblogging networks.