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Robustness of power-law networks: its assessment and optimization

Author

Listed:
  • Huiling Zhang

    (University of Florida)

  • Yilin Shen

    (University of Florida)

  • My T. Thai

    (University of Florida
    Ton Duc Thang University)

Abstract

Many practical complex networks, such as the Internet, WWW and social networks, are discovered to follow power-law distribution in their degree sequences, i.e., the number of nodes with degree $$i$$ i in these networks is proportional to $$i^{-\beta }$$ i - β for some exponential factor $$\beta > 0$$ β > 0 . However, these networks also expose their vulnerabilities to a great number of threats such as adversarial attacks on the Internet, cyber-crimes on the WWW or malware propagations on social networks. Although power-law networks have been found robust under random attacks and vulnerable to intentional attacks via experimental observations, how to better understand their vulnerabilities from a theoretical point of view still remains open. In this paper, we study the vulnerability of power-law networks under random attacks and adversarial attacks using the in-depth probabilistic analysis on the theory of random power-law graph models. Our results indicate that power-law networks are able to tolerate random failures if their exponential factor $$\beta $$ β is $$ 2.5$$ β > 2.5 the network robustness is unpredictable since it depends on the specific attacking strategy.

Suggested Citation

  • Huiling Zhang & Yilin Shen & My T. Thai, 2016. "Robustness of power-law networks: its assessment and optimization," Journal of Combinatorial Optimization, Springer, vol. 32(3), pages 696-720, October.
  • Handle: RePEc:spr:jcomop:v:32:y:2016:i:3:d:10.1007_s10878-015-9893-7
    DOI: 10.1007/s10878-015-9893-7
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    References listed on IDEAS

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    1. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    2. T. N. Dinh & M. T. Thai & H. T. Nguyen, 2014. "Bound and exact methods for assessing link vulnerability in complex networks," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 3-24, July.
    3. Kitsak, Maksim & Havlin, Shlomo & Paul, Gerald & Riccaboni, Massimo & Pammolli, Fabio & Stanley, H. Eugene, 2007. "Betweenness Centrality of Fractal and Non-Fractal Scale-Free Model Networks and Tests on Real Networks," MPRA Paper 15907, University Library of Munich, Germany.
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