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Target control of complex networks

Author

Listed:
  • Jianxi Gao

    (Northeastern University)

  • Yang-Yu Liu

    (Northeastern University
    Center for Cancer Systems Biology, Dana-Farber Cancer Institute
    Brigham and Women's Hospital, Harvard Medical School)

  • Raissa M. D'Souza

    (Complexity Sciences Center, University of California
    Santa Fe Institute)

  • Albert-László Barabási

    (Northeastern University
    Center for Cancer Systems Biology, Dana-Farber Cancer Institute
    Brigham and Women's Hospital, Harvard Medical School)

Abstract

Controlling large natural and technological networks is an outstanding challenge. It is typically neither feasible nor necessary to control the entire network, prompting us to explore target control: the efficient control of a preselected subset of nodes. We show that the structural controllability approach used for full control overestimates the minimum number of driver nodes needed for target control. Here we develop an alternate ‘k-walk’ theory for directed tree networks, and we rigorously prove that one node can control a set of target nodes if the path length to each target node is unique. For more general cases, we develop a greedy algorithm to approximate the minimum set of driver nodes sufficient for target control. We find that degree heterogeneous networks are target controllable with higher efficiency than homogeneous networks and that the structure of many real-world networks are suitable for efficient target control.

Suggested Citation

  • Jianxi Gao & Yang-Yu Liu & Raissa M. D'Souza & Albert-László Barabási, 2014. "Target control of complex networks," Nature Communications, Nature, vol. 5(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6415
    DOI: 10.1038/ncomms6415
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    Cited by:

    1. Meng, Tao & Duan, Gaopeng & Li, Aming & Wang, Long, 2023. "Control energy scaling for target control of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    2. Liu, Jie & Schonfeld, Paul M. & Shuai, Chunyan & He, Mingwei & Wang, Kelvin C.P., 2022. "The controllability of China’s high-speed rail network in terms of delivering emergency supplies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    3. Lu Zhong & Mamadou Diagne & Qi Wang & Jianxi Gao, 2022. "Vaccination and three non-pharmaceutical interventions determine the dynamics of COVID-19 in the US," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
    4. Li, Xiang & Li, Guoqi & Gao, Leitao & Li, Beibei & Xiao, Gaoxi, 2024. "Sufficient control of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    5. Farahmand, Hamed & Liu, Xueming & Dong, Shangjia & Mostafavi, Ali & Gao, Jianxi, 2022. "A Network Observability Framework for Sensor Placement in Flood Control Networks to Improve Flood Situational Awareness and Risk Management," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    6. Yin, Haofei & Zhang, Aobo & Zeng, An, 2023. "Identifying hidden target nodes for spreading in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).

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