Quantification of network structural dissimilarities based on network embedding

iScience

Chuang Liu¹  Zi-Ke Zhang²  Zhipeng Wang²  Xiu-Xiu Zhan² 

[1] Corresponding author;Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China;

关键词: Computer science;    Network;    Network topology;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: 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 DeepWalk. 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.

【 授权许可】

Unknown