A Novel Convex Combination-Based Mixed Centrality Measure for Identification of Influential Nodes in Complex Networks

Abstract

Exploring the significance of popular node’s impact in complex networks yields numerous advantages, such as improving network resilience and accelerating information dissemination. While conventional centrality measures accurately quantify individual node importance, they may inadvertently overlook certain properties of influential nodes. The quest for new centrality metrics has garnered substantial research due to their theoretical relevance and practical applicability in real-world network scenarios. The existing research has predominantly focused on designing centrality metrics based on the local and/or global topological characteristics of nodes. Nevertheless, these metrics do not consider the nodes located in the intermediary zones between the inner and outer regions of a network, resulting in reduced effectiveness when applied to large-scale network scenarios. To address these challenges, we have introduced a novel convex framework to formulate the Convex Mixed Centrality (COMC) measure. This metric aims to overcome the limitations of traditional centrality metrics by incorporating insights from both local and global network dynamics, thus enhancing its ability to identify influential nodes across various network regions. To prove the efficacy of our proposed measure, we utilize the Susceptible-Infected-Recovered (SIR) and Independent Cascade (IC) models, alongside the Kendall tau metric. Extensive simulation experiments conducted on various real-world datasets demonstrate that the COMC measure outperforms conventional centrality indices in terms of spreading efficiency, all while maintaining comparable computational complexity.

Keywords

• Complex networks
• local and global centrality
• isolating centrality
• connected components
• critical nodes