ABSTRACT
We investigate targeted attacks to important links in scale-free networks and propose simple and efficient heuristics strategies to mitigate the damage. In order to reduce the computational burden, we focus on heuristics that make use of local information for the most part. Using model scale-free networks and under the constraint of an invariant degree distribution, we show by numerical simulation that our approach allows to enhance the network robustness notably, as measured by the integrity of the largest connected component. We also show that the approach is effective on a couple of larger real-world networks.
- R. Albert, H. Jeong, and A.-L. Barabási. 2000. Error and attack tolerance of complex networks. Nature 406 (2000), 378--382.Google Scholar
- A.-L. Barabási and R. Albert. 1999. Emergence of scaling in random networks. Science 286, 5439 (1999), 509--512.Google ScholarCross Ref
- A. Beygelzimer, G. Grinstein, R. Linsker, and I. Rish. 2005. Improving network robustness by edge modification. Physica A: Statistical Mechanics and its Applications 357, 3--4 (2005), 593--612.Google ScholarCross Ref
- H. Chan and L. Akoglu. 2016. Optimizing network robustness by edge rewiring: a general framework. Data Mining and Knowledge Discovery 30, 5 (2016), 1395--1425.Google ScholarDigital Library
- Y.-J. Deng, Y.-Q. Li, R.-R. Yin, H.-Y. Zhao, and B. Liu. 2020. Efficient measurement model for critical nodes based on edge clustering coefficients and edge betweenness. Wireless Networks 26, 4 (2020), 2785--2795.Google ScholarCross Ref
- P. Holme, B. J. Kin, C. N. Yoon, and S. K. Han. 2002. Attack vulnerability of complex networks. Phys. Rev. E 65 (2002), 056109.Google ScholarCross Ref
- J. Kunegis. 2013. KONECT - The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion. 1343--1350. http://http://konect.cc/networks/Google Scholar
- V. Latora and M. Marchiori. 2001. Efficient behavior of small-world networks. Phys. Rev. Lett. 87 (2001), 198701.Google ScholarCross Ref
- J. Liu, M. Zhou, S. Wang, and P. Liu. 2017. A comparative study of network robustness measures. Frontiers of Computer Science 11, 4 (2017), 568--584.Google ScholarDigital Library
- M. E. J. Newman. 2018. Networks: An Introduction. Oxford University Press, Oxford, UK. second edition.Google ScholarCross Ref
- C. Pizzuti, A. Socievole, and P. Van Mieghem. 2019. Comparative network robustness evaluation of link attacks. In International Conference on Complex Networks and Their Applications. Springer, 735--746.Google Scholar
- C. M. Schneider, A. A. Moreira, J. S. Andrade Jr, S. Havlin, and H. J. Herrmann. 2011. Mitigation of malicious attacks on networks. Proceedings of the National Academy of Sciences 108, 10 (2011), 3838--3841.Google ScholarCross Ref
- A. Zeng and W. Liu. 2012. Enhancing network robustness against malicious attacks. Physical Review E 85, 6 (2012), 066130.Google ScholarCross Ref
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