Identification of node centrality based on Laplacian energy of networks

Identifying influential spreaders in complex networks is a crucial issue that can help control the propagation process in complex networks. Existing methods propose sub-stantial improvements over many classical centrality methods. Over the years, some researchers have applied concepts of graph energy to node recognition. Based on this, we propose a new node centrality - the third Laplacian energy centrality (LC). This method is to define the centrality of nodes from a global perspective and can be simplified into a local formula while inheriting the advantages of the global property, which greatly reduces the time complexity. By assuming that the propagation process in the network follows a susceptible-infected-recovery (SIR) model, we conduct extensive experiments in 13 real networks, and compare the performance of LC with a range of other centrality measures. The results show that LC is more reasonable and superior than other methods in identifying influential spreaders. (c) 2022 Elsevier B.V. All rights reserved.