Based on the fact that the traditional probability distribution entropy describing a local feature of the system cannot effectively capture the global topology variations of the network, some indicators constructed by the network adjacency matrix and Laplacian matrix come into being. Specifically, these measures are based on the eigenvalues of the scaled Laplace matrix, the eigenvalues of the network communicability matrix, and the spectral entropy based on information diffusion that has been proposed recently, respectively. In this article, we systematically study the dependence of these measures on the topological structure of the network. We prove from various aspects that spectral entropy has a better ability to identify the global topology than the traditional distribution entropy. Furthermore, the indicator based on the eigenvalues of the network communicability matrix achieves good results in some aspects while, overall, the spectral entropy is able to identify network topology variations from a global perspective.

中文翻译：

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基于谱熵的网络拓扑变化识别。

基于描述系统局部特征的传统概率分布熵不能有效地捕获网络的全局拓扑变化这一事实，由网络邻接矩阵和拉普拉斯矩阵构成的一些指标应运而生。具体地，这些措施分别基于缩放的拉普拉斯矩阵的特征值，网络可通信性矩阵的特征值以及基于最近已经提出的基于信息扩散的谱熵。在本文中，我们系统地研究了这些措施对网络拓扑结构的依赖性。我们从各个方面证明，频谱熵比传统的分布熵具有更好的识别全局拓扑的能力。此外，