Node Ranking in Complex Networks Based on LeaderRank and Modes Similaritya

The effect of important nodes in complex networks on the structure and function of the networks causes widespread concern. This paper presents a SRank algorithm based on LeaderRank and nodes similarity which is used to measure the interaction between nodes. The simulation of SIR model and Spearman's correlation coefficient on real social networks show that the SRankalgorithm preforms better on identifying influential nodes both in directed and undirected networks, compared with the other four classical algorithms.