Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24–25, 2019, Proceedings

Research Article

Improving Complex Network Controllability via Link Prediction

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  • @INPROCEEDINGS{10.1007/978-3-030-32388-2_8,
        author={Ran Wei and Weiwei Yuan and Donghai Guan and Asad Khattak and Muhammad Fahim},
        title={Improving Complex Network Controllability via Link Prediction},
        proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings},
        proceedings_a={MLICOM},
        year={2019},
        month={10},
        keywords={Network controllability Link prediction Complex networks},
        doi={10.1007/978-3-030-32388-2_8}
    }
    
  • Ran Wei
    Weiwei Yuan
    Donghai Guan
    Asad Khattak
    Muhammad Fahim
    Year: 2019
    Improving Complex Network Controllability via Link Prediction
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-32388-2_8
Ran Wei,*, Weiwei Yuan,*, Donghai Guan,*, Asad Khattak1,*, Muhammad Fahim2,*
  • 1: Zayed University
  • 2: Innopolis University
*Contact email: ran_dlml@163.com, yuanweiwei@nuaa.edu.cn, dhguan@nuaa.edu.cn, Asad.Khattak@zu.ac.ae, m.fahim@innopolis.ru

Abstract

Complex network is a network structure composed of a large number of nodes and complex relationships between these nodes. Using complex network can model many systems in real life. The individual in the system corresponds to the node in the network and the relationship between these individuals corresponds to the edge in the network. The controllability of complex networks is to study how to enable the network to arrive at the desired state from any initial state by external input signals. The external input signals transmit to the whole network through some nodes in the network, and these nodes are called driver node. For the study of controllability of complex network, it is mainly to judge whether the network is controllable or not and how to select the appropriate driver nodes at present. If a network has a high controllability, the network will be easy to control. However, complex networks are vulnerable and will cause declining of controllability. Therefore, we propose in this paper a link prediction-based method to make the network more robust to different modes of attacking. Through experiments we have validated the effectiveness of the proposed method.