Testing community structure for hypergraphs
Author:
Affiliation:
1. Department of Statistics, North Dakota State University
2. Department of Mathematical Sciences, Texas Tech University
3. Department of Biostatistics, New York University
4. Department of Mathematical Sciences, New Jersey Institute of Technology
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference59 articles.
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2. Bickel, P. J. and Sarkar, P. (2016). Hypothesis testing for automated community detection in networks. J. R. Stat. Soc. Ser. B. Stat. Methodol. 78 253–273.
3. Zhao, Y., Levina, E. and Zhu, J. (2011). Community extraction for social networks. Proc. Natl. Acad. Sci. USA 108 7321–7326.
4. Amini, A. A., Chen, A., Bickel, P. J. and Levina, E. (2013). Pseudo-likelihood methods for community detection in large sparse networks. Ann. Statist. 41 2097–2122.
5. FORTUNATO, S. (2010). Community detection in graphs. Phys. Rep. 486 75–174.
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