Identifying critical nodes in complex networks based on distance Laplacian energy

Identifying critical nodes in complex networks is a fundamental problem, it plays a crucial role in stabilizing the performance of the network structure and propagating information. Majority of the existing studies are built by directly considering the topology of the network. In this paper, a new vertex centrality called distance Laplacian centrality (DLC) is proposed for critical nodes identification from the perspective of graph energy. This method incorporates the vertex's transfer degree, considers the position of nodes in the network from a global perspective, and measures the importance of a node using the relative variation of the distance Laplacian energy responding to the deletion of the node from the network. To validate the performance and applicability of the proposed method, this paper compares DLC with other methods through susceptible-infected-recovered (SIR) model on different real networks. The experimental results demonstrate that DLC has better performance in terms of influence, distinguishing ability relevance and ranking accuracy, and can effectively recognize critical nodes in complex networks.