Convex Isolating Clustering Centrality to Discover the Influential Nodes in Large Scale Networks

Abstract

Ranking influential nodes within complex networks offers invaluable insights into a wide array of phenomena ranging from disease management to information dissemination and optimal routing in real-time networking applications. Centrality measures, which quantify the importance of nodes based on network properties and relationships of nodes within the network, are instrumental in achieving this task. These measures are typically classified into local and global centralities. Global measures consider the overall structure and connectivity patterns. However, they often suffer from high computational complexity in large-scale networks. On the other hand, local measures focus on the immediate neighborhood of each node, potentially overlooking global information. To address these challenges, we propose a novel metric called Isolating Clustering Centrality (ISCL), which leverages a convex combination approach. By introducing a convex tuning parameter, ISCL enhances the applicability and adaptability of centrality measures across a wide range of real-world network applications. In this study, we assess the efficacy of the proposed measure using real-world network datasets and simulate the spreading process using susceptible-infected-removed (SIR) and independent cascade (IC) models. Our extensive results demonstrate that ISCL significantly improves spreading efficiency compared to conventional and recent centrality measures, while also maintaining better computational efficiency in large-scale complex networks.

Keywords

• Isolating centrality
• clustering coefficient
• convex tuning parameter
• influential nodes
• complex networks