Fast Network Community Detection With Profile-Pseudo Likelihood Methods-Reference-Cited by-同舟云学术

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Fast Network Community Detection With Profile-Pseudo Likelihood Methods

Published:2021-12-08 Issue: Volume: Page:1-14
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Wang Jiangzhou¹²^ORCID,Zhang Jingfei³,Liu Binghui¹,Zhu Ji⁴,Guo Jianhua¹

Affiliation:

1. School of Mathematics and Statistics & KLAS, Northeast Normal University, Jilin, China

2. Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China

3. Department of Management Science, University of Miami, Coral Gables, FL

4. Department of Statistics, University of Michigan, Ann Arbor, MI

Funder

National Key R&D Program of China

NSFC

China Postdoctoral Science Foundation

the Special Fund for Key Laboratories of Jilin Province, China

NSF

the National Natural Science Foundation of China

the Fundamental Research Funds for the Central Universities grant

the Key Laboratory of Applied Statistics of MOE

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2021.1996378

Reference42 articles.

1. Exact Recovery in the Stochastic Block Model

2. Entrywise eigenvector analysis of random matrices with low expected rank

3. The political blogosphere and the 2004 U.S. election

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1. GBTM: Community detection and network reconstruction for noisy and time-evolving data;Information Sciences;2024-09

2. Distributed Pseudo-Likelihood Method for Community Detection in Large-Scale Networks;ACM Transactions on Knowledge Discovery from Data;2024-06-19

3. Two-sample test for stochastic block models via the largest singular value;Communications in Statistics - Theory and Methods;2024-03-27

4. Recent advances on mechanisms of network generation: Community, exchangeability, and scale‐free properties;WIREs Computational Statistics;2024-03

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