LAUSR

Because of interdependence between different network layers, interdependent networks are more fragile than single-layer networks, and large-scale iterative paralysis occurs easily. How to seek nodes whose removal can effectively dismantle networks has attracted great research attention. In this paper, a novel optimal dismantling strategy Maximum Entropy Centrality (EC) and overlapping betweenness (OB) based on residual theory (ECOB) is proposed. In the ECOB, the residual theory is used to detect the highest influence nodes according to the quality of the residual networks. In addition, to make sorting more accurate, EC and OB parameters are both considered in the node selection mechanism. Simulation shows that the ECOB strategy performs much better than existing methods both in artificial interdependent networks and real-world interdependent networks. This is thanks to the introduced ECOB node selection algorithm with proper parameter criterions.