Hierarchical Community Detection by Recursive Partitioning-Reference-Cited by-同舟云学术

Hierarchical Community Detection by Recursive Partitioning

Published:2020-11-24 Issue: Volume: Page:1-18
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Li Tianxi¹,Lei Lihua²,Bhattacharyya Sharmodeep³,Van den Berge Koen⁴⁵,Sarkar Purnamrita⁶,Bickel Peter J.⁴,Levina Elizaveta⁷

Affiliation:

1. Department of Statistics, University of Virginia, Charllottesville, VA

2. Department of Statistics, Stanford University, Stanford, CA

3. Department of Statistics, Oregon State University, Corvallis. OR

4. Department of Statistics, University of California, Berkeley, Berkeley, CA

5. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium

6. Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX

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

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2020.1833888

Reference84 articles.

1. Abbe, E., Fan, J., Wang, K., and Zhong, Y. (2017), “Entrywise Eigenvector Analysis of Random Matrices With Low Expected Rank,” arXiv no. 1709.09565.

2. Pseudo-likelihood methods for community detection in large sparse networks

3. On semidefinite relaxations for the block model

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