TY  - JOUR
AU  - Zhou, Andu
AU  - Maletić, Slobodan
AU  - Zhao, Yi
PY  - 2018
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/7738
AB  - Efficient robustness and fault tolerance of complex network is significantly influenced by its connectivity, commonly modeled by the structure of pairwise relations between network elements, i.e., nodes. Nevertheless, aggregations of nodes build higher-order structures embedded in complex network, which may be more vulnerable when the fraction of nodes is removed. The structure of higher-order aggregations of nodes can be naturally modeled by simplicial complexes, whereas the removal of nodes affects the values of topological invariants, like the number of higher-dimensional holes quantified with Betti numbers. Following the methodology of percolation theory, as the fraction of nodes is removed, new holes appear, which have the role of merger between already present holes. In the present article, relationship between the robustness and homological properties of complex network is studied, through relating the graph-theoretical signatures of robustness and the quantities derived from topological invariants. The simulation results of random failures and intentional attacks on networks suggest that the changes of graph-theoretical signatures of robustness are followed by differences in the distribution of number of holes per cluster under different attack strategies. In the broader sense, the results indicate the importance of topological invariants research for obtaining further insights in understanding dynamics taking place over complex networks.
T2  - Physica A: Statistical Mechanics and Its Applications
T1  - Robustness and percolation of holes in complex networks
VL  - 502
SP  - 459
EP  - 468
DO  - 10.1016/j.physa.2018.02.149
ER  - 

@article{
author = "Zhou, Andu and Maletić, Slobodan and Zhao, Yi",
year = "2018",
abstract = "Efficient robustness and fault tolerance of complex network is significantly influenced by its connectivity, commonly modeled by the structure of pairwise relations between network elements, i.e., nodes. Nevertheless, aggregations of nodes build higher-order structures embedded in complex network, which may be more vulnerable when the fraction of nodes is removed. The structure of higher-order aggregations of nodes can be naturally modeled by simplicial complexes, whereas the removal of nodes affects the values of topological invariants, like the number of higher-dimensional holes quantified with Betti numbers. Following the methodology of percolation theory, as the fraction of nodes is removed, new holes appear, which have the role of merger between already present holes. In the present article, relationship between the robustness and homological properties of complex network is studied, through relating the graph-theoretical signatures of robustness and the quantities derived from topological invariants. The simulation results of random failures and intentional attacks on networks suggest that the changes of graph-theoretical signatures of robustness are followed by differences in the distribution of number of holes per cluster under different attack strategies. In the broader sense, the results indicate the importance of topological invariants research for obtaining further insights in understanding dynamics taking place over complex networks.",
journal = "Physica A: Statistical Mechanics and Its Applications",
title = "Robustness and percolation of holes in complex networks",
volume = "502",
pages = "459-468",
doi = "10.1016/j.physa.2018.02.149"
}

Zhou, A., Maletić, S.,& Zhao, Y.. (2018). Robustness and percolation of holes in complex networks. in Physica A: Statistical Mechanics and Its Applications, 502, 459-468.
https://doi.org/10.1016/j.physa.2018.02.149

Zhou A, Maletić S, Zhao Y. Robustness and percolation of holes in complex networks. in Physica A: Statistical Mechanics and Its Applications. 2018;502:459-468.
doi:10.1016/j.physa.2018.02.149 .

Zhou, Andu, Maletić, Slobodan, Zhao, Yi, "Robustness and percolation of holes in complex networks" in Physica A: Statistical Mechanics and Its Applications, 502 (2018):459-468,
https://doi.org/10.1016/j.physa.2018.02.149 . .