Node center identification in complex networks based on Jensen–Shannon divergence

Abstract

The evaluation of node centrality remains a critical challenge in the field of complex network research. This paper proposes a novel method, the $JSD$ method, which integrates local and global information to measure node centrality. The method employs Shannon entropy to quantify local centrality and Jensen–Shannon ($JS$) divergence to compute inter-community distances, thereby assessing topological differences between communities and measuring global centrality. Experiments conducted on both real and random networks evaluated the impact of central nodes identified by the $JSD$ method on network efficiency. Simulation results demonstrate that in networks with distinct community structures, the $JSD$ method provides more accurate node centrality measurements compared to the $BC$, $CCI$, $CBC$, $COMM$, and $CI$ methods. Additionally, the repetition frequency of central ranking nodes indicates that the $JSD$ method effectively distinguishes the centrality of different nodes. Finally, the paper discusses the impact of different combination methods on measurement performance, revealing that incorporating additional community information further enhances the method’s effectiveness.