  Identifying and quantifying structural dissimilarities between complex
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, degree and graphlet, which may only contain part of
the topological information. Therefore, we propose an efficient network
comparison method based on network embedding, i.e., \textit{DeepWalk}, which
considers the global structural information. In detail, we calculate the
distance between nodes through the vector extracted by \textit{DeepWalk} and
quantify the network dissimilarity by spectral entropy based Jensen-Shannon
divergences of the distribution of the node distances. Experiments on both
synthetic and empirical data show that our method outperforms the baseline
methods and can distinguish networks perfectly by only using the global
embedding based distance distribution. In addition, we show that our method can
capture network properties, e.g., average shortest path length and link
density. Moreover, the experiments of modularity further implies the
functionality of our method.
