A hierarchical approach for influential node ranking in complex social networks

Due to the rapid extension of social networks in recent years, a new potential has emerged for global spreading of messages and effective broadcasting of news. Identification of influential nodes within a network is now seen as a key factor for bringing this potential into action. k-shell is a measure for detection of node influence, and has already been used in some successful algorithms in this field. However, k-shell does not provide enough information about the topological positions of the nodes, and the present paper seeks to present a special hierarchical measure for dealing with this issue. Along with introduction of the above measure, it is shown that it can be used for detecting and ranking node influence. The experiments done on real-world and artificial networks demonstrate that the proposed approach can rank the influence of nodes more accurately than other approaches.