We develop a method to generate robust networks against malicious
attacks, as well as to substantially improve the robustness of a given
network by swapping edges and keeping the degree distribution fixed. The
method, based on persistence of the size of the largest cluster during
attacks, was applied to several types of networks with broad degree
distributions, including a real network-the Internet. We find that our
method can improve the robustness significantly. Our results show that
robust networks have a novel `onion-like' topology consisting of a core
of highly connected nodes hierarchically surrounded by rings of nodes
with decreasing degree.
%0 Journal Article
%1 WOS:000286629000030
%A Herrmann, Hans J
%A Schneider, Christian M
%A Moreira, Andre A
%A Jr., Jose S Andrade
%A Havlin, Shlomo
%C TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
%D 2011
%I IOP PUBLISHING LTD
%J JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
%K and communication; dynamics; information network networks; optimization; reconstruction} robust stochastic supply {network
%R 10.1088/1742-5468/2011/01/P01027
%T Onion-like network topology enhances robustness against malicious
attacks
%X We develop a method to generate robust networks against malicious
attacks, as well as to substantially improve the robustness of a given
network by swapping edges and keeping the degree distribution fixed. The
method, based on persistence of the size of the largest cluster during
attacks, was applied to several types of networks with broad degree
distributions, including a real network-the Internet. We find that our
method can improve the robustness significantly. Our results show that
robust networks have a novel `onion-like' topology consisting of a core
of highly connected nodes hierarchically surrounded by rings of nodes
with decreasing degree.
@article{WOS:000286629000030,
abstract = {We develop a method to generate robust networks against malicious
attacks, as well as to substantially improve the robustness of a given
network by swapping edges and keeping the degree distribution fixed. The
method, based on persistence of the size of the largest cluster during
attacks, was applied to several types of networks with broad degree
distributions, including a real network-the Internet. We find that our
method can improve the robustness significantly. Our results show that
robust networks have a novel `onion-like' topology consisting of a core
of highly connected nodes hierarchically surrounded by rings of nodes
with decreasing degree.},
added-at = {2022-05-23T20:00:14.000+0200},
address = {TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND},
author = {Herrmann, Hans J and Schneider, Christian M and Moreira, Andre A and Jr., Jose S Andrade and Havlin, Shlomo},
biburl = {https://www.bibsonomy.org/bibtex/26e5be9c8e653a0a6ed612b5518a9b357/ppgfis_ufc_br},
doi = {10.1088/1742-5468/2011/01/P01027},
interhash = {54dc9fb764b3eb869e769fe12269eb8b},
intrahash = {6e5be9c8e653a0a6ed612b5518a9b357},
issn = {1742-5468},
journal = {JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT},
keywords = {and communication; dynamics; information network networks; optimization; reconstruction} robust stochastic supply {network},
publisher = {IOP PUBLISHING LTD},
pubstate = {published},
timestamp = {2022-05-23T20:00:14.000+0200},
title = {Onion-like network topology enhances robustness against malicious
attacks},
tppubtype = {article},
year = 2011
}