Mutually connected components (MCCs) play an important role as a measure of
resilience in the study of interconnected networks. Despite their importance,
an efficient algorithm to obtain physical properties of all MCCs during the
removals of links is not available. Here, using a well-known fully-dynamic
graph algorithm, we propose an efficient algorithm to accomplish this. We show
that the time complexity of this algorithm is approximately
\$O(N^1.2)\$, which is more efficient than the brute-force algorithm
with complexity \$O(N^2)\$. We anticipate this algorithm to allow
simulations with complex dynamic rules to research a size regime that was not
permitted before.
%0 Journal Article
%1 Hwang2015Efficient
%A Hwang, S.
%A Choi, S.
%A Lee, Deokjae
%A Kahng, B.
%D 2015
%J Physical Review E
%K percolation algorithms interdependent-networks
%N 2
%R 10.1103/PhysRevE.91.022814
%T Efficient algorithm to compute mutually connected components in interdependent networks
%U http://dx.doi.org/10.1103/PhysRevE.91.022814
%V 91
%X Mutually connected components (MCCs) play an important role as a measure of
resilience in the study of interconnected networks. Despite their importance,
an efficient algorithm to obtain physical properties of all MCCs during the
removals of links is not available. Here, using a well-known fully-dynamic
graph algorithm, we propose an efficient algorithm to accomplish this. We show
that the time complexity of this algorithm is approximately
\$O(N^1.2)\$, which is more efficient than the brute-force algorithm
with complexity \$O(N^2)\$. We anticipate this algorithm to allow
simulations with complex dynamic rules to research a size regime that was not
permitted before.
@article{Hwang2015Efficient,
abstract = {Mutually connected components (MCCs) play an important role as a measure of
resilience in the study of interconnected networks. Despite their importance,
an efficient algorithm to obtain physical properties of all MCCs during the
removals of links is not available. Here, using a well-known fully-dynamic
graph algorithm, we propose an efficient algorithm to accomplish this. We show
that the time complexity of this algorithm is approximately
\$\mathcal{O}(N^{1.2})\$, which is more efficient than the brute-force algorithm
with complexity \$\mathcal{O}(N^{2})\$. We anticipate this algorithm to allow
simulations with complex dynamic rules to research a size regime that was not
permitted before.},
added-at = {2019-06-10T14:53:09.000+0200},
archiveprefix = {arXiv},
author = {Hwang, S. and Choi, S. and Lee, Deokjae and Kahng, B.},
biburl = {https://www.bibsonomy.org/bibtex/20eb1a38a74b9126663ce952937b69fb2/nonancourt},
citeulike-article-id = {13346477},
citeulike-linkout-0 = {http://dx.doi.org/10.1103/PhysRevE.91.022814},
citeulike-linkout-1 = {http://arxiv.org/abs/1409.1147},
citeulike-linkout-2 = {http://arxiv.org/pdf/1409.1147},
day = 23,
doi = {10.1103/PhysRevE.91.022814},
eprint = {1409.1147},
interhash = {8ead824c688700484f48493b598b8272},
intrahash = {0eb1a38a74b9126663ce952937b69fb2},
issn = {1550-2376},
journal = {Physical Review E},
keywords = {percolation algorithms interdependent-networks},
month = feb,
number = 2,
posted-at = {2014-09-04 10:26:06},
priority = {2},
timestamp = {2019-08-01T15:38:00.000+0200},
title = {{Efficient algorithm to compute mutually connected components in interdependent networks}},
url = {http://dx.doi.org/10.1103/PhysRevE.91.022814},
volume = 91,
year = 2015
}