InfVIKOR: A hybrid Decision-Making computational approach to identify influential nodes in complex networks

Abstract

Abstract Identifying influential nodes in complex networks remains a significant challenge in network analysis. In this direction, one attractive challenge is to characterize the spreading capabilities of nodes, which could serve as potential regulators of the network. While node centrality methods have been widely used for identifying such nodes, they are often tailored to specific problems. In this research work, a new method InfVIKOR is proposed aimed at accurately identifying influential nodes and addressing bias inherent in single-measure evaluations. This method utilizes a Multi-Criteria Decision Making (MCDM) approach called VIKOR, which integrates multiple parameters to effectively identify influential nodes. The method uses the centrality measure as a criterion with proper optimization method to construct group utility function of the complex network, and then quick sort algorithm is applied to rank the nodes according to their influence score derived from the group utility measure. InfVIKOR prioritizes influential nodes to achieve a balanced combination of efficacy and efficiency. To evaluate the effectiveness of the method, the Susceptible-Infected (SI) model is employed to simulate communication propagation across six real-world networks. The experimental findings underscore the accuracy and efficacy of the proposed method. Further, this method can be used in any hierarchical scale free networks.