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Optimization of targeted node set in complex networks under percolation and selection

Yang Liu, Xi Wang, and Jürgen Kurths
Phys. Rev. E 98, 012313 – Published 23 July 2018

Abstract

Most of the existing methods for the robustness and targeted immunization problems can be viewed as greedy strategies, which are quite efficient but readily induce a local optimization. In this paper, starting from a percolation perspective, we develop two strategies, the relationship-related (RR) strategy and the prediction relationship (PR) strategy, to avoid a local optimum only through the investigation of interrelationships among nodes. Meanwhile, RR combines the sum rule and the product rule from explosive percolation, and PR holds the assumption that nodes with high degree are usually more important than those with low degree. In this manner our methods have a better capability to collapse or protect a network. The simulations performed on a number of networks also demonstrate their effectiveness, especially on large real-world networks where RR fragments each of them into the same size of the giant component; however, RR needs only less than 90% of the number of nodes which are necessary for the most excellent existing methods.

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  • Received 22 February 2018
  • Revised 1 June 2018

DOI:https://doi.org/10.1103/PhysRevE.98.012313

©2018 American Physical Society

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Authors & Affiliations

Yang Liu1,2,*, Xi Wang3, and Jürgen Kurths1,4,5

  • 1Potsdam Institute for Climate Impact Research, 14412 Potsdam, Germany
  • 2Department of Computer Science, Technische Universität Berlin, 10587 Berlin, Germany
  • 3Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
  • 4Department of Physics, Humboldt University Berlin, 12489 Berlin, Germany
  • 5Institute of Applied Physics, Russian Academy of Science, 603950 Nizhny Novgorod, Russia

  • *yangliu@pik-potsdam.de

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Issue

Vol. 98, Iss. 1 — July 2018

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