Finding influential nodes in social networks based on neighborhood correlation coefficient

Abstract

Finding the most influential nodes in social networks has significant applications. A number of methods have been recently proposed to estimate influentiality of nodes based on their structural location in the network. It has been shown that the number of neighbors shared by a node and its neighbors accounts for determining its influence. In this paper, an improved cluster rank approach is presented that takes into account common hierarchy of nodes and their neighborhood set. A number of experiments are conducted on synthetic and real networks to reveal effectiveness of the proposed ranking approach. We also consider ground-truth influence ranking based on Susceptible–Infected–Recovered model, on which performance of the proposed ranking algorithm is verified. The experiments show that the proposed method outperforms state-of-the-art algorithms.