Spreading the information in complex networks: Identifying a set of top-N influential nodes using network structure

Abstract

The real world contains many complex networks, including research networks, social networks, biological networks, and transport networks. Real-world complex networks are unconstrained and can be characterized as undirected and unweighted. Understanding and controlling the process of information propagation in such networks is significant for decision-making activities and has many uses, such as disease control, market advertising, rumor control, and innovation propagation. Identifying the influencers in complex networks is an important activity, as influencers play a key role in spreading information to aid the decision-making process. In this study, we consider the problem of identifying a set of top-N influential nodes for spreading the information in undirected and unweighted networks using the network structure in the absence of domain-specific knowledge. In this study, we propose a novel method that computes the ranking scores of the nodes in the network and considers the influence of other nodes simultaneously when forming the set of top-N influential nodes. The proposed method is different from other methods of identification of influential nodes in the network, in that it takes into consideration the position of the nodes in the network while computing the ranking score, thereby preventing the clustering of important nodes, which hampers the information flow. Experiments are performed using several real-world complex networks to demonstrate the effectiveness of the proposed method.