The structural properties of network system components often display strikingly similar behavior when probed at a macroscopic perspective. Those structural properties, largely determining their dynamic behavior, are revealed by the mesoscopic structure of the underlying networks. To demonstrate this empirical conclusion, it is necessary to develop a set of tools to accurately quantify the structural similarity between network nodes. In this paper, we propose a method to measure nodes’ similarity based on network communicability. Precisely, the approach takes Jensen-Shannon divergence between the communicability sequences of nodes as the difference measure and then obtains the similarity between nodes. We use some real-world networks and artificial networks as test objects, and evaluate the rationality of the method through the topological structure behavior and dynamical behavior of similar nodes respectively. Interestingly, the similar nodes obtained in our framework have very similar dynamical behaviors, which is crucial because the dynamic behaviors of nodes are highly dependent on the mesoscopic structure of the underlying networks. Furthermore, compared with previous methods, the method presented in this paper can more accurately quantify the similarity between nodes from a global perspective.

当从宏观角度进行探究时，网络系统组件的结构特性通常显示出惊人的相似行为。这些结构特性在很大程度上决定了它们的动态行为，这是由底层网络的介观结构揭示的。为了证明这一经验结论，有必要开发一套工具来准确地量化网络节点之间的结构相似性。本文提出了一种基于网络可通信性的节点相似度度量方法。精确地，该方法将节点的可通信性序列之间的Jensen-Shannon散度作为差异度量，然后获得节点之间的相似性。我们使用一些现实世界的网络和人工网络作为测试对象，并分别通过相似节点的拓扑结构行为和动力行为来评价该方法的合理性。有趣的是，在我们的框架中获得的相似节点具有非常相似的动态行为，这是至关重要的，因为节点的动态行为高度依赖于基础网络的介观结构。此外，与以前的方法相比，本文提出的方法可以从全局角度更准确地量化节点之间的相似性。