A Novel Ranking Scheme for Identifying Influential Nodes in Complex Networks

Identifying influential nodes in complex networks, such as social networks, is crucial in many applications including information dissemination, virus protection , community detection, etc., and many methods have been proposed for identifying influential nodes. Most existing methods only consider one single attribute of nodes, which for some applications may not be sufficient, and can lead to low resolution among nodes. In this paper, we propose a novel method for identifying influential nodes−CI-based gravity model (CIGM)−which integrates the integrated degree and I-shell to evaluate the impact of nodes in an all-around manner. It aims at offering a more comprehensive measure of the node’s influence than the known methods. We conducted comparison experiments for CIGM against ten baseline methods using twelve real-world datasets, in terms of the SIR model, the Kendall’s correlation coefficient, CCDF and monotonicity index. The experimental results on twelve real-world networks show that CIGM not only identifies influential nodes effectively but also captures nodes of strategic importance, those that help enhance performance in information dissemination and network connectivity, suggesting that CIGM holds advantageousness and broader applicability over the existing schemes.