2011/12/27 by Tamás Nepusz, Tamás Vicsek · 440 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Applied mathematics #Artificial intelligence #Bioinformatics #Biological network #Biology #Centrality #Complex Network Analysis Techniques #Complex dynamics #Complex network #Complex system #Computer science #Controllability #Degree distribution #Dynamical systems theory #Enhanced Data Rates for GSM Evolution #Focus (optics) #Gene Regulatory Network Analysis #Mathematics #Network controllability #Network dynamics #Neural Networks Stability and Synchronization #Physics #Process (computing) #Scale (ratio) #Scale-free network #Simple (philosophy) #Statistical physics #Topology (electrical circuits) #Uncorrelated #cond-mat.stat-mech #cs.SI #physics.soc-ph
paper · pdf · doi:10.1038/nphys2327
published in Nature Physics 8(7), 568-573 (Nature Portfolio) · Preprint. 24 pages, 4 figures, 2 tables. Source code available at http://github.com/ntamas/netctrl
arxiv created 2011/12/27 · openalex publication_date 2012/05/27 · arxiv updated 2012/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The interaction of distinct units in physical, social, biological and technological systems naturally gives rise to complex network structures. Networks have constantly been in the focus of research for the last decade, with considerable advances in the description of their structural and dynamical properties. However, much less effort has been devoted to studying the controllability of the dynamics taking place on them. Here we introduce and evaluate a dynamical process defined on the edges of a network, and demonstrate that the controllability properties of this process significantly differ from simple nodal dynamics. Evaluation of real-world networks indicates that most of them are more controllable than their randomized counterparts. We also find that transcriptional regulatory networks are particularly easy to control. Analytic calculations show that networks with scale-free degree distributions have better controllability properties than uncorrelated networks, and positively correlated in- and out-degrees enhance the controllability of the proposed dynamics.