A novel semi-local centrality to identify influential nodes in complex networks by integrating multidimensional factors

Abstract

This study addresses the critical problem of identifying influential nodes in complex networks, a task that plays a pivotal role in understanding network dynamics, optimizing information spread, and controlling epidemic outbreaks. Although semi-local centrality metrics are a valid approach for identifying influential nodes, they face challenges such as inefficiency when dealing with large-scale networks and neglecting semantic relationships, often relying on unidimensional criteria that limit their effectiveness. To tackle this challenge, this study presents a novel Semi-Local Centrality metric designed to identify influential nodes in complex networks by incorporating Multidimensional Factors (SLCMF). SLCMF combines structural, social, and semantic factors to find seed nodes in complex networks. To improve scalability, SLCMF utilizes distributed local subgraphs and redefines semi-local centrality by employing the average shortest path theory. Additionally, SLCMF incorporates a semantic graph embedding model by an augmented graph to capture distant and latent relationships among nodes. Extensive experiments on real-world networks demonstrate the effectiveness and efficiency of the proposed centrality metric, showcasing its superior performance in ranking influential nodes. Specifically, SLCMF outperforms the best traditional and advanced centrality metrics, improving Kendall's correlation coefficient by 8.94% and 1.61%, respectively. Additionally, the proposed metric demonstrates enhanced efficiency, reducing runtime by 4.7% and 0.21% compared to the top-performing traditional and advanced metrics, respectively.