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

2025-03-15

AI Summary

Existing centrality-based approaches for identifying influential node groups in complex networks suffer from overlapping influence regions and weakened collective effects. Method: We propose the Weighted Cycle (WCycle) metric—the first to jointly model topological cyclic structures, dynamic edge weights, and node behavioral features—enabling discovery of spatially dispersed, structurally diverse, and low-redundancy key groups. Our framework integrates graph-theoretic weighted cycle enumeration and aggregation, behavior-aware weighted fusion, and propagation simulation (SIR/IC models) with multi-dimensional benchmarking. Results: Evaluated on six real-world networks against five baseline methods, WCycle achieves 12.7%–34.2% higher influence coverage, significantly improving structural discriminability, cost-effectiveness, algorithmic robustness, and scalability.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

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 group, thereby reducing influence redundancy and enhancing network-wide propagation. Extensive experiments on six real-world networks demonstrate WCycle's superior performance compared to five 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.

Problem

• Identifies influential node groups in complex networks
• Overcomes limitations of traditional centrality-based methods
• Enhances network-wide propagation and reduces influence redundancy

Innovation

• Weighted Cycle (WCycle) assesses node importance comprehensively.
• WCycle identifies spatially dispersed key node groups effectively.
• WCycle enhances network-wide propagation and reduces redundancy.