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Controllability Analysis Of Complex Networks Using Statistical Random Sampling

Author

Listed:
  • BABAK RAVANDI

    (Network Science Institute, Center for Complex Network Research, Northeastern University, Boston, MA 02115, USA)

  • FOROUGH S. ANSARI

    (Department of Computer Science and Engineering, Georgia State University, Alpharetta, GA 30022, USA)

  • FATMA MILI

    (College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

Abstract

Large complex dynamical systems behave in ways that reflect their structure. There are many applications where we need to control these systems by bringing them from their current state to a desired state. Affecting the state of these systems is done by communications with its key elements, called driver nodes, in reference to their representation as a network of nodes. Over the past decades, much focus has been paid on analytical approaches to derive optimal control configurations based on the concept of Minimum Driver node Sets (MDSs) for directed complex networks. However, the underlying control mechanisms of many complex systems rely on quickly controlling a major subspace of a system. In this work, we ask how complex networks behave if driver nodes are randomly selected? We seek to understand and employ the statistical characteristics of MDSs to randomly select driver nodes and analyze the controllability properties of complex network. We propose an algorithm to build Random Driver node Sets (RDSs) and analyze their controllable subspace, the minimum time needed to control, and the cardinality of RDSs. Through our evaluations on a variety of synthetic and real-world networks, we show RDSs can quickly and effectively control a major subspace of networks.

Suggested Citation

  • Babak Ravandi & Forough S. Ansari & Fatma Mili, 2020. "Controllability Analysis Of Complex Networks Using Statistical Random Sampling," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-15, January.
    • Handle: RePEc:wsi:acsxxx:v:22:y:2020:i:07n08:n:s0219525919500127
    DOI: 10.1142/S0219525919500127
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    References listed on IDEAS

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    4. Magnus Egerstedt, 2011. "Degrees of control," Nature, Nature, vol. 473(7346), pages 158-159, May.
    5. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2012. "Control Centrality and Hierarchical Structure in Complex Networks," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
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