  The stochastic block model is a popular tool for detecting community
structures in network data. Detecting the difference between two community
structures is an important issue for stochastic block models. However, the
two-sample test has been a largely under-explored domain, and too little work
has been devoted to it. In this article, based on the maximum entry--wise
deviation of the two centered and rescaled adjacency matrices, we propose a
novel test statistic to test two samples of stochastic block models. We prove
that the null distribution of the proposed test statistic converges in
distribution to a Gumbel distribution, and we show the change of the two
samples from stochastic block models can be tested via the proposed method.
Then, we show that the proposed test has an asymptotic power guarantee against
alternative models. One noticeable advantage of the proposed test statistic is
that the number of communities can be allowed to grow linearly up to a
logarithmic factor. Further, we extend the proposed method to the
degree-corrected stochastic block model. Both simulation studies and real-world
data examples indicate that the proposed method works well.
