%0 Journal Article %1 WOS:000317205200005 %A Schneider, Christian M %A Araujo, Nuno A M %A Herrmann, Hans J %C ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA %D 2013 %I AMER PHYSICAL SOC %J PHYSICAL REVIEW E %K imported %N 4 %R 10.1103/PhysRevE.87.043302 %T Algorithm to determine the percolation largest component in interconnected networks %V 87 %X Interconnected networks have been shown to be much more vulnerable to random and targeted failures than isolated ones, raising several interesting questions regarding the identification and mitigation of their risk. The paradigm to address these questions is the percolation model, where the resilience of the system is quantified by the dependence of the size of the largest cluster on the number of failures. Numerically, the major challenge is the identification of this cluster and the calculation of its size. Here, we propose an efficient algorithm to tackle this problem. We show that the algorithm scales as O(N logN), where N is the number of nodes in the network, a significant improvement compared to O(N-2) for a greedy algorithm, which permits studying much larger networks. Our new strategy can be applied to any network topology and distribution of interdependencies, as well as any sequence of failures. DOI: 10.1103/PhysRevE.87.043302

@article{WOS:000317205200005, abstract = {Interconnected networks have been shown to be much more vulnerable to random and targeted failures than isolated ones, raising several interesting questions regarding the identification and mitigation of their risk. The paradigm to address these questions is the percolation model, where the resilience of the system is quantified by the dependence of the size of the largest cluster on the number of failures. Numerically, the major challenge is the identification of this cluster and the calculation of its size. Here, we propose an efficient algorithm to tackle this problem. We show that the algorithm scales as O(N logN), where N is the number of nodes in the network, a significant improvement compared to O(N-2) for a greedy algorithm, which permits studying much larger networks. Our new strategy can be applied to any network topology and distribution of interdependencies, as well as any sequence of failures. DOI: 10.1103/PhysRevE.87.043302}, added-at = {2022-05-23T20:00:14.000+0200}, address = {ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA}, author = {Schneider, Christian M and Araujo, Nuno A M and Herrmann, Hans J}, biburl = {https://www.bibsonomy.org/bibtex/23aebffc3530a06928fa8667cc6a530e5/ppgfis_ufc_br}, doi = {10.1103/PhysRevE.87.043302}, interhash = {c2e3c58ecb8fbf9bc51c78124878967a}, intrahash = {3aebffc3530a06928fa8667cc6a530e5}, issn = {2470-0045}, journal = {PHYSICAL REVIEW E}, keywords = {imported}, number = 4, publisher = {AMER PHYSICAL SOC}, pubstate = {published}, timestamp = {2022-05-23T20:00:14.000+0200}, title = {Algorithm to determine the percolation largest component in interconnected networks}, tppubtype = {article}, volume = 87, year = 2013 }