Wang Zhipeng, Zhan Xiu-Xiu, Liu Chuang, Zhang Zi-Ke
Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China.
College of Media and International Culture, Zhejiang University, Hangzhou 310058, PR China.
iScience. 2022 May 23;25(6):104446. doi: 10.1016/j.isci.2022.104446. eCollection 2022 Jun 17.
Quantifying structural dissimilarities between networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path and degree, which only contain part of the topological information. Therefore, we propose an efficient network comparison method based on network embedding, which considers the global structural information. In detail, we first construct a distance matrix for each network based on the distances between node embedding vectors derived from . Then, we define the dissimilarity between two networks based on Jensen-Shannon divergence of the distance distributions. Experiments on both synthetic and empirical networks show that our method outperforms the baseline methods and can distinguish networks well. In addition, we show that our method can capture network properties, e.g., average shortest path length and link density. Moreover, the experiment of modularity further implies the functionality of our method.
量化网络之间的结构差异是网络科学中的一个基本且具有挑战性的问题。以往的网络比较方法是基于结构特征，如最短路径长度和度，而这些仅包含部分拓扑信息。因此，我们提出了一种基于网络嵌入的高效网络比较方法，该方法考虑了全局结构信息。具体而言，我们首先基于从……导出的节点嵌入向量之间的距离为每个网络构建一个距离矩阵。然后，我们基于距离分布的 Jensen-Shannon 散度定义两个网络之间的差异。在合成网络和实证网络上的实验表明，我们的方法优于基线方法，并且能够很好地区分网络。此外，我们表明我们的方法可以捕捉网络属性，例如平均最短路径长度和链接密度。而且，模块化实验进一步暗示了我们方法的功能。
