TY - JOUR
T1 - Cascading failures in anisotropic interdependent networks of spatial modular structures
AU - Vaknin, Dana
AU - Bashan, Amir
AU - Braunstein, Lidia A.
AU - Buldyrev, Sergey V.
AU - Havlin, Shlomo
N1 - Publisher Copyright:
© 2021 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft
PY - 2021/11
Y1 - 2021/11
N2 - The structure of real-world multilayer infrastructure systems usually exhibits anisotropy due to constraints of the embedding space. For example, geographical features like mountains, rivers and shores influence the architecture of critical infrastructure networks. Moreover, such spatial networks are often non-homogeneous but rather have a modular structure with dense connections within communities and sparse connections between neighboring communities. When the networks of the different layers are interdependent, local failures and attacks may propagate throughout the system. Here we study the robustness of spatial interdependent networks which are both anisotropic and heterogeneous. We also evaluate the effect of localized attacks having different geometrical shapes. We find that anisotropic networks are more robust against localized attacks and that anisotropic attacks, surprisingly, even on isotropic structures, are more effective than isotropic attacks.
AB - The structure of real-world multilayer infrastructure systems usually exhibits anisotropy due to constraints of the embedding space. For example, geographical features like mountains, rivers and shores influence the architecture of critical infrastructure networks. Moreover, such spatial networks are often non-homogeneous but rather have a modular structure with dense connections within communities and sparse connections between neighboring communities. When the networks of the different layers are interdependent, local failures and attacks may propagate throughout the system. Here we study the robustness of spatial interdependent networks which are both anisotropic and heterogeneous. We also evaluate the effect of localized attacks having different geometrical shapes. We find that anisotropic networks are more robust against localized attacks and that anisotropic attacks, surprisingly, even on isotropic structures, are more effective than isotropic attacks.
KW - Network theory
KW - Percolation theory
KW - Statistical physics
UR - http://www.scopus.com/inward/record.url?scp=85118920599&partnerID=8YFLogxK
U2 - 10.1088/1367-2630/ac2e3c
DO - 10.1088/1367-2630/ac2e3c
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SN - 1367-2630
VL - 23
JO - New Journal of Physics
JF - New Journal of Physics
IS - 11
M1 - 113001
ER -