How to identify important nodes in complex networks? It is still an open problem. Many methods have been proposed to tackle this problem. The main contribution of this paper is to propose a method to identify important nodes based on local volume dimension (LVD). If the LVD of the node is lower, the node is more important. Promising results of experiments on four real-world networks compared with six methods under both Susceptible–Infected (SI) model and Susceptible–Infected–Recovered (SIR) model validate and demonstrate the effectiveness of the proposed method.

中文翻译：

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局部体积维度：复杂网络中重要节点识别的新方法

如何识别复杂网络中的重要节点？这仍然是一个悬而未决的问题。已经提出了许多方法来解决这个问题。本文的主要贡献是提出了一种基于局部体积维度（LVD）的重要节点识别方法。如果节点的LVD较低，则节点更重要。四个真实世界网络的实验结果与易感感染（SI）模型和易感感染恢复（SIR）模型下的六种方法相比，验证并证明了所提出方法的有效性。