Information Gain Model for Efficient Influential Node Identification in Social Networks

摘要

Influential node detection in social networks has become a vital approach to defining some key players in a network. Many approaches have developed applications of such social network analyses for viral marketing, law enforcement, and collaborative support systems for communities using clustering algorithms or centrality measures. One of the most efficient ways to identify influential nodes in a network is to find centralities of the nodes based on their information gain, which takes into account the information gains of their neighbouring nodes as well. In this paper, we propose a hybrid model of influential node search based on such centralities like the degree centrality, betweenness centrality and information gain of the nodes to provide a more precise measure of influence in any network.