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Two-sample test for stochastic block models via the largest singular value

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成果类型:
期刊论文
作者:
Fu, Kang;Hu, Jianwei;Keita, Seydou;Liu, Hang
通讯作者:
Hu, JW
作者机构:
[Liu, Hang; Hu, Jianwei; Keita, Seydou; Fu, Kang] Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China.
[Fu, Kang] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan, Peoples R China.
[Hu, Jianwei] Cent China Normal Univ, Key Lab Nonlinear Anal & Applicat, Minist Educ, Wuhan, Peoples R China.
通讯机构:
[Hu, JW ] C
Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China.
语种:
英文
关键词:
Adjacency matrix;network data;singular value;stochastic block model;two-sample test
期刊:
Communications in Statistics - Theory and Methods
ISSN:
0361-0926
年:
2024
基金类别:
National Natural Science Foundation of China [12171187, 12371261]
机构署名:
本校为第一且通讯机构
院系归属:
数学与统计学学院
摘要:
The stochastic block model is widely used for detecting community structures in network data. However, the research interest in much of the literature focuses on the study of one sample of stochastic block models. Detecting the difference between the two community structures is a less studied issue for stochastic block models. In this article, we propose a novel test statistic based on the largest singular value of a residual matrix obtained by subtracting the geometric mean of two estimated block mean effects from the sum of two observed adjacency matrices. We prove that the null distribution...

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