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Fragmenting networks by targeting collective influencers at a mesoscopic level

Author

Listed:
  • Teruyoshi Kobayashi

    (Graduate School of Economics, Kobe University)

  • Naoki Masuda

    (Department of Engineering Mathematics, University of Bristol)

Abstract

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

Suggested Citation

  • Teruyoshi Kobayashi & Naoki Masuda, 2016. "Fragmenting networks by targeting collective influencers at a mesoscopic level," Discussion Papers 1616, Graduate School of Economics, Kobe University.
  • Handle: RePEc:koe:wpaper:1616
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    File URL: http://www.econ.kobe-u.ac.jp/RePEc/koe/wpaper/2016/1616-6.pdf
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    Cited by:

    1. Ren, Baoan & Zhang, Yu & Chen, Jing & Shen, Lincheng, 2019. "Efficient network disruption under imperfect information: The sharpening effect of network reconstruction with no prior knowledge," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 196-207.
    2. de Abreu, Carolina & Gonçalves, Sebastián & da Cunha, Bruno Requião, 2021. "Empirical determination of the optimal attack for fragmentation of modular networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).

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    Keywords

    network; community structure; epidemics;

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