The effect analysis of critical subgroup based on K-core on public outbreak as big data

Now, information diffusion based on the most influential single node is a mainstream research perspective. However, very little attention has been given to the issue how the essential subgroups in the network are influenced the spread of information. Here we use k-shell decomposition method to measure critical subgroup in the Micro-blog, which examine its efficacy in realistic information spreading. With the increase of its degree of activity, the network information will burst in varying degrees. We have not only verified this method in three actual data sets, but also compared with the random strategy and the maximum value of the strategy. Our experimental results show that our algorithm not only finds a more influential group, but also has a higher level of influence than other strategies. This result has significance for the efficient transportation, epidemic control, and emergency evacuation.