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  • 标题:Estimating the minimum control count of random network models
  • 本地全文:下载
  • 作者:Derek Ruths ; Justin Ruths
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2016
  • 卷号:6
  • DOI:10.1038/srep19818
  • 出版社:Springer Nature
  • 摘要:The study of controllability of complex networks has introduced the minimum number of controls required for full controllability as a new network measure of interest. This network measure, like many others, is non-trivial to compute. As a result, establishing the significance of minimum control counts (MCCs) in real networks using random network null models is expensive. Here we derive analytic estimates for the expected MCCs of networks drawn from three commonly-used random network models. Our estimates show good agreement with exact control counts. Furthermore, the analytic expressions we derive offer insights into the structures within each random network model that induce the need for controls.
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