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We make the first steps towards generalizing the theory of stochastic block models, in the sparse regime, towards a model where the discrete community structure is replaced by an underlying geometry.
Matrix Analysis
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E. Mossel · 2001
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Information flow on trees
E. Mossel and Y. Peres · 2003
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Survey: information flow on trees
E. Mossel · 2004
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Accurate error bounds for the eigenvalues of the kernel matrix
M. L. Braun · 2006
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Elements of functional analysis
F. Hirsch and G. Lacombe · 2012
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User-friendly tail bounds for sums of random matrices
J. Tropp · 2012
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Robust estimation of latent tree graphical models: inferring hidden states with inexact parameters
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Universally consistent vertex classification for latent positions graphs
M. Tang, D. L. Sussman, and C. E. Priebe · 2013
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D. L. Sussman, M. Tang, and C. E. Priebe · 2014
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On the time for Brownian motion to visit every point on a circle
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S. Galhotra, A. Mazumdar, S. Pal, and B. Saha · 2017
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Concentration and regularization of random graphs
C. M. Le, E. Levina, and R. Vershynin · 2017
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Community detection and stochastic block models
E. Abbe · 2018
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Contextual stochastic block models
Y. Deshpande, S. Sen, A. Montanari, and E. Mossel · 2018
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Concentration of random graphs and application to community detection
C. M. Le, E. Levina, and R. Vershynin · 2018
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Relative concentration bounds for the kernel matrix spectrum
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Adaptive estimation of nonparametric geometric graphs
Y. De Castro, C. Lacour, and T. M. P. Ngoc · 2017
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E. A. Valdivia · 2018
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Latent distance estimation for random geometric graphs
E. Araya Valdivia and Y. De Castro · 2019
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