Identify Influential Spreaders in Asymmetrically Interacting Multiplex Networks

Identifying the most influential spreaders is crucial to use limited resource to control spreading in the network. Existing studies of ranking node importance in multilayer networks are mostly based on the network structure, while neglecting how the structural and dynamical couplings of multiple layers impact the spreading influence of nodes in coevolving dynamics. Here we investigate on the identification of influential spreaders in the information-disease coupled spreading dynamics on multiplex networks. Firstly we explicitly reveal that three interlayer coupling factors, which are the two-layer relative spreading rate, the interlayer coupling strength and the interlayer degree correlation, significantly impact the spreading influence of a node in the contact layer where disease spreads. The suppression effect from the information layer makes the structural centrality of nodes in the contact layer fail to predict their influence. Then by mapping the coevolving spreading processes into percolation and using the message-passing approach, we propose a method to calculate the outbreak size of disease starting from a single seed node, which is used to estimate the spreading influence of the node and identify the most influential spreaders in coevolving dynamics on multiplex networks. Our work gives a feasible framework to investigate critical nodes in multiplex networks.