Efficient Method for Improving the Spreading Efficiency in Small-World Networks and Assortative Scale-Free Networks

Abstract

Many complex systems are abstractly regarded as complex networks in the study of complicated problems. The research field of information spreading in complex networks has attracted extensive interest. Many spreading strategies have been proposed for improving the spreading efficiency in complex networks. However, the strategies differ in terms of performance in various complex networks. In this paper, a hybrid and effective method for improving the spreading efficiency in small-world networks and assortative scale-free networks is proposed. The proposed method can be applied to solve the essential problem of low spreading efficiency due to spreading to small-degree vertices. The proposed method combines two strategies: 1) a set of top small-degree vertices are specified as the initial spreaders and 2) vertices preferentially spread information to large-degree neighbors. Sixty-eight groups of Monte Carlo experiments are conducted in three real complex networks and seventeen synthetic complex networks. According to the experimental results and theoretical analysis, the proposed method is efficient for improving the spreading efficiency in small-world networks and assortative scale-free networks. Moreover, in assortative scale-free networks, the improvement in the spreading efficiency that is realized via the proposed method increases with the assortativity coefficient.