期刊论文

Zhu, Ji

英文

Journal of the American Statistical Association

0162-1459

2021

116

535

1373-1382

10.1080/01621459.2020.1722676

Hu is partially supported by the National Natural Science Foundation of China (nos. 11471136, 11571133) and the Key Laboratory of Applied Statistics of MOE (KLAS) grants (nos. 130026507, 130028612). Qin is partially supported by the National Natural Science Foundation of China (no. 11871237). Yan is partially supported by the National Natural Science Foundation of China (no. 11771171), the Fundamental Research Funds for the Central Universities, and the Key Laboratory of Applied Statistics of MOE (KLAS) grants (nos. 130026507, 130028612). Zhu is partially supported by the National Science Foundation (DMS 1407698 and DMS 1821243). Zhang is partially supported by the National Science Foundation (DMS 2015190).

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Abstract–The stochastic block model is widely used for detecting community structures in network data. How to test the goodness of fit of the model is one of the fundamental problems and has gained growing interests in recent years. In this article, we propose a novel goodness-of-fit test based on the maximum entry of the centered and rescaled adjacency matrix for the stochastic block model. One noticeable advantage of the proposed test is that the number of communities can be allowed to grow linearly with the number of nodes ignoring a logari...