www.nature.com/nature/journal/v473/n7346/full/nature10011.html

Preview meta tags from the www.nature.com website.

Linked Hostnames

32

Thumbnail

Search Engine Appearance

Google

https://www.nature.com/nature/journal/v473/n7346/full/nature10011.html

Controllability of complex networks - Nature

The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes. Control theory can be used to steer engineered and natural systems towards a desired state, but a framework to control complex self-organized systems is lacking. Can such networks be controlled? Albert-László Barabási and colleagues tackle this question and arrive at precise mathematical answers that amount to 'yes, up to a point'. They develop analytical tools to study the controllability of an arbitrary complex directed network using both model and real systems, ranging from regulatory, neural and metabolic pathways in living organisms to food webs, cell-phone movements and social interactions. They identify the minimum set of driver nodes whose time-dependent control can guide the system's entire dynamics ( http://go.nature.com/wd9Ek2 ). Surprisingly, these are not usually located at the network hubs.



Bing

Controllability of complex networks - Nature

https://www.nature.com/nature/journal/v473/n7346/full/nature10011.html

The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes. Control theory can be used to steer engineered and natural systems towards a desired state, but a framework to control complex self-organized systems is lacking. Can such networks be controlled? Albert-László Barabási and colleagues tackle this question and arrive at precise mathematical answers that amount to 'yes, up to a point'. They develop analytical tools to study the controllability of an arbitrary complex directed network using both model and real systems, ranging from regulatory, neural and metabolic pathways in living organisms to food webs, cell-phone movements and social interactions. They identify the minimum set of driver nodes whose time-dependent control can guide the system's entire dynamics ( http://go.nature.com/wd9Ek2 ). Surprisingly, these are not usually located at the network hubs.



DuckDuckGo

https://www.nature.com/nature/journal/v473/n7346/full/nature10011.html

Controllability of complex networks - Nature

The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes. Control theory can be used to steer engineered and natural systems towards a desired state, but a framework to control complex self-organized systems is lacking. Can such networks be controlled? Albert-László Barabási and colleagues tackle this question and arrive at precise mathematical answers that amount to 'yes, up to a point'. They develop analytical tools to study the controllability of an arbitrary complex directed network using both model and real systems, ranging from regulatory, neural and metabolic pathways in living organisms to food webs, cell-phone movements and social interactions. They identify the minimum set of driver nodes whose time-dependent control can guide the system's entire dynamics ( http://go.nature.com/wd9Ek2 ). Surprisingly, these are not usually located at the network hubs.

  • General Meta Tags

    124
    • title
      Controllability of complex networks | Nature
    • title
      Close banner
    • title
      Close banner
    • X-UA-Compatible
      IE=edge
    • applicable-device
      pc,mobile
  • Open Graph Meta Tags

    6
    • og:url
      https://www.nature.com/articles/nature10011
    • og:type
      article
    • og:site_name
      Nature
    • og:title
      Controllability of complex networks - Nature
    • og:description
      Control theory can be used to steer engineered and natural systems towards a desired state, but a framework to control complex self-organized systems is lacking. Can such networks be controlled? Albert-László Barabási and colleagues tackle this question and arrive at precise mathematical answers that amount to 'yes, up to a point'. They develop analytical tools to study the controllability of an arbitrary complex directed network using both model and real systems, ranging from regulatory, neural and metabolic pathways in living organisms to food webs, cell-phone movements and social interactions. They identify the minimum set of driver nodes whose time-dependent control can guide the system's entire dynamics ( http://go.nature.com/wd9Ek2 ). Surprisingly, these are not usually located at the network hubs.
  • Twitter Meta Tags

    6
    • twitter:site
      @nature
    • twitter:card
      summary_large_image
    • twitter:image:alt
      Content cover image
    • twitter:title
      Controllability of complex networks
    • twitter:description
      Nature - Control theory can be used to steer engineered and natural systems towards a desired state, but a framework to control complex self-organized systems is lacking. Can such networks be...
  • Item Prop Meta Tags

    4
    • position
      1
    • position
      2
    • position
      3
    • publisher
      Springer Nature
  • Link Tags

    15
    • alternate
      https://www.nature.com/nature.rss
    • apple-touch-icon
      /static/images/favicons/nature/apple-touch-icon-f39cb19454.png
    • canonical
      https://www.nature.com/articles/nature10011
    • icon
      /static/images/favicons/nature/favicon-48x48-b52890008c.png
    • icon
      /static/images/favicons/nature/favicon-32x32-3fe59ece92.png

Emails

1

Links

263