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 substantial 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.