Projective, sparse and learnable latent position network models
Author:
Affiliation:
1. Department of Statistics, University of Connecticut
2. Department of Statistics and Data Science, Carnegie Mellon University
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference46 articles.
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2. Hoff, P. D., Raftery, A. E. and Handcock, M. S. (2002). Latent space approaches to social network analysis. J. Amer. Statist. Assoc. 97 1090–1098.
3. Handcock, M. S., Raftery, A. E. and Tantrum, J. M. (2007). Model-based clustering for social networks. J. Roy. Statist. Soc. Ser. A 170 301–354.
4. Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Rev. 45 167–256.
5. Borgs, C., Chayes, J. T., Cohn, H. and Holden, N. (2017). Sparse exchangeable graphs and their limits via graphon processes. J. Mach. Learn. Res. 18 Paper No. 210, 71.
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