From transportation networks to complex infrastructures, and to social andeconomic networks, a large variety of systems can be described in terms ofmultiplex networks formed by a set of nodes interacting through differentnetwork layers. Network robustness, as one of the most successful applicationareas of complex networks, has also attracted great interest in boththeoretical and empirical researches. However, the vast majority of existingresearches mainly focus on the robustness of single-layer networks aninterdependent networks, how multiplex networks respond to potential attack isstill short of further exploration. Here we study the robustness of multiplexnetworks under two attack strategies: layer node-based random attack and layernode-based targeted attack. A theoretical analysis framework is proposed tocalculate the critical threshold and the size of giant component of multiplexnetworks when a fraction of layer nodes are removed randomly or intentionally.Via numerous simulations, it is unveiled that the theoretical method canaccurately predict the threshold and the size of giant component, irrespectiveof attack strategies. Moreover, we also compare the robustness of multiplexnetworks under multiplex node-based attack and layer node-based attack, andfind that layer node-based attack makes multiplex networks more vulnerable,regardless of average degree and underlying topology. Our finding may shed newlight on the protection of multiplex networks.
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