Interdependence of dynamical signals and topology: Detecting the influential nodes in networks

2005/05/10 by Lei Yang, Liang Huang, Yang, Lei +5
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Neural Networks Stability and Synchronization #Opinion Dynamics and Social Influence #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.dis-nn #cond-mat.stat-mech

paper · pdf · doi:10.48550/arxiv.cond-mat/0505236

4 pages, 7 figures

arxiv created 2005/05/10 · openalex publication_date 2005/05/10 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

By studying varies dynamical processes, including coupled maps, cellular automata and coupled differential equations, on five different kinds of known networks, we found a positive relation between signal correlation and node's degree. Thus a method of identifying influential nodes in dynamical systems is proposed, its validity is studied, and potential applications on real systems are discussed.

Related

• Physics Informed Topology Learning in Networks of Linear Dynamical\n Systems
• Functional Dynamical Structures in Complex Systems: an\n Information-Theoretic Approach
• Topological identification in networks of dynamical systems
• Topology Identification under Spatially Correlated Noise
• A frequency approach to topological identification and graphical modeling