Weighted cycle-based identification of influential node groups in complex networks

Wenxin Zheng, Wenfeng Shi, Tianlong Fan and Linyuan Lü

Physica A: Statistical Mechanics and its Applications, 2025, vol. 677, issue C

Abstract: Identifying influential node groups in complex networks is crucial for optimizing information dissemination, epidemic control, and viral marketing. However, traditional centrality-based methods often focus on individual nodes, resulting in overlapping influence zones and diminished collective effectiveness. To overcome these limitations, we propose Weighted Cycle (WCycle), a novel indicator that incorporates basic cycle structures and node behavior traits (edge weights) to comprehensively assess node importance. WCycle effectively identifies spatially dispersed and structurally diverse key node groups, thereby reducing influence redundancy and enhancing network-wide propagation. Extensive experiments on six real-world networks demonstrate WCycle’s superior performance compared to nine benchmark methods across multiple evaluation dimensions, including influence propagation efficiency, structural differentiation, and cost-effectiveness. The findings highlight WCycle’s robustness and scalability, establishing it as a promising tool for complex network analysis and practical applications requiring effective influence maximization.

Keywords: Complex networks; Weighted cycle; Node group selection; Influence maximization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:677:y:2025:i:c:s0378437125004820

DOI: 10.1016/j.physa.2025.130830

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