Adjusted chi-square test for degree-corrected block models
提出一种针对度修正随机块模型的拟合优度检验，基于调整卡方统计量，适用于节点度差异大的稀疏网络，可用于确定社区数量并检验模型是否拟合数据。
We propose a goodness-of-fit test for degree-corrected stochastic block models (DCSBM). The test is based on an adjusted chi-square statistic for measuring equality of means among groups of n multinomial distributions with d1,…,dn observations. In the context of network models, the number of multinomials, n, grows much faster than the number of observations, di, corresponding to the degree of node i, hence the setting deviates from classical asymptotics. We show that a simple adjustment allows the statistic to converge in distribution, under null, as long as the harmonic mean of {di} grows to infinity. When applied sequentially, the test can also be used to determine the number of communities. The test operates on a compressed version of the adjacency matrix, conditional on the degrees, and as a result is highly scalable to large sparse networks. We incorporate a novel idea of compressing the rows based on a (K+1)-community assignment when testing for K communities. This approach increases the power in sequential applications without sacrificing computational efficiency, and we prove its consistency in recovering the number of communities. Since the test statistic does not rely on a specific alternative, its utility goes beyond sequential testing and can be used to simultaneously test against a wide range of alternatives outside the DCSBM family. In particular, we prove that the test is consistent against a general family of latent-variable network models with community structure. We show the effectiveness of the approach by extensive numerical experiments with simulated and real data. In particular, applying the test to the Facebook-100 data set, a collection of one hundred social networks, we find that a DCSBM with a small number of communities (say <25) is far from a good fit in almost all cases.