Influential node ranking in social networks based on neighborhood diversity

Abstract

Social networks have significant role in distribution of ideas and advertisement. Discovering the most influential nodes has been a hot topic in the field of social networks analysis and mining. This manuscript proposes novel algorithms for this purpose based on neighborhood diversity. We introduce two new influential node ranking algorithms that use diversity of the neighbors of each node in order to obtain its ranking value. They are applied on a number of real-world networks and compared with state-of-the-art algorithms. Our experimental results reveal effectiveness of the proposed algorithms.