Finding Influential Nodes in Complex Networks Using Nearest Neighborhood Trust Value

Abstract

Information spreading in complex networks is an emerging topic in many applications such as social leaders, rumour control, viral marketing, and opinion monitor. Finding the influential nodes plays a pivotal role for information spreading in complex network. This is because influential nodes have capable to spread more information in compared with other nodes. Currently, there are many centrality measures proposed to identify the influential nodes in the complex network such as degree, betweenness, closeness, semi-local centralities and page-rank etc. These centrality measures are defined based on the local and/or global information of nodes in the network. Sheng et al. [18] propose centrality measure based on the information between nodes and structure of network. Inspired by this measure, we propose the nearest neighborhood trust page rank (NTPR) based on structural information of neighbours and nearest neighbours. We proposed the measure based on the similarity between nodes, degree ratio, trust value of neighbours and nearest neighbours. We also perform on various real world network with proposed centrality measure for finding the influential nodes. Furthermore, we also compare the results with existing basic centrality measures.