Identification of influential nodes from social networks based on Enhanced Degree Centrality Measure

Abstract

A social network is a set of relationships and interactions among social entities such as individuals, organizations, and groups. The social network analysis is one of the major topics in the ongoing research field. The major problem regarding the social network is finding the most influential objects or persons. Identification of most influential nodes in a social network is a tedious task as large numbers of new users join the network every day. The most commonly used method is to consider the social network as a graph and find the most influential nodes by analyzing it. The degree centrality method is node based and has the advantage of easy identification of most influential nodes. In this paper a method called “Enhanced Degree Centrality Measure” is proposed which integrates clustering co-efficient value along with node based degree centrality. The enhanced degree centrality measure is applied to three different datasets which are obtained from the Facebook to analyze the performance. The response obtained is compared with existing methods such as degree centrality method and SPIN algorithm. By comparison, it is found that highest number of active nodes identified by the proposed method is 64 when compared with SPIN algorithm which identifies only 55.