Cascades Tolerance of Scale-Free Networks with Attack Cost

DOI

10.2991/ijcis.10.1.93How to use a DOI?

Keywords

Network robustness; Cascading failures; Genetic algorithm; Attack cost

Abstract

Network robustness against cascades is a major topic in the fields of complex networks. In this paper, we propose an attack-cost-based cascading failure model, where the attack cost of nodes is positively related to its degree. We compare four attacking strategies: the random removal strategy (RRS), the low-degree removal strategy (LDRS), the high-degree removal strategy (HDRS) and the genetic algorithm removal strategy (GARS). It is shown that the network robustness against cascades is heavily affected by attack costs and the network exhibits the weakest robustness under GARS. We also explore the relationship between the network robustness and tolerance parameter under these attacking strategies. The simulation results indicate that the critical value of tolerance parameter under GARS is greatly larger than that of other attacking strategies. Our work can supply insight into the robustness and vulnerability of complex networks corresponding to cascading failures.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Chen Hong
AU  - Nai-Yu Yin
AU  - Ning He
AU  - Oriol Lordan
AU  - Jose Maria Sallan
PY  - 2017
DA  - 2017/09/14
TI  - Cascades Tolerance of Scale-Free Networks with Attack Cost
JO  - International Journal of Computational Intelligence Systems
SP  - 1330
EP  - 1336
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.10.1.93
DO  - 10.2991/ijcis.10.1.93
ID  - Hong2017
ER  -