A mixture model for random graphs

By Jean-Jacques Daudin, Franck Picard, Stéphane Robin. Statistics and computing, 2008

502 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mathematical Physics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bcd31f26f3cb55b1181c0dcc4d83c4e1d2ef056c

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Citations in the corpus

Nine quick tips for analyzing network data (2019, methodology): “The stochastic block model (SBM) is a popular method based on this definition [32, 34], which has shown to be relevant for the analysis of some biological networks (to highlight the complex architecture of connectomes [33] or functional groups in ecological networks [35]).”
Identifiability of parameters in latent structure models with many observed variables (2008, background): “A nonexhaustive bibliography includes [12, 18, 36, 41].”
Hierarchical block structures and high-resolution model selection in large networks (2013, methodology): “…one of the many principled methods one could use to do model selection, which include, e.g., Bayesian model selection via integrated likelihood [21, 38, 39, 41, 42, 55], likelihood ratios [56] or more approximative methods such as Bayesian information criterion (BIC) [57] and Akaike information…”
Statistical clustering of temporal networks through a dynamic stochastic block model (2015, methodology): “We refer to Daudin et al. (2008) for an expression of the ICL in the static SBM that shows an analogous difference”
Asymptotic Normality of Maximum Likelihood and its Variational Approximation for Stochastic Blockmodels (2012, methodology): “We note that J simpliﬁes to This formula indicates that, at least for the complete parameterization, a lo-cal optimum to J can be tractably computed for moderate n and K using the EM algorithm as in Daudin, Picard and Robin (2008).”
Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model (2011, methodology): “A variational approach has been proposed by Daudin et al. (2008) to remedy this computational burden.”
Statistical Models for Social Networks (2011, background): “(1983), Snijders and Nowicki (1994),Nowicki and Snijders (2001), and Daudin et al. (2008) is a model in which there is a node-level categorical latent variable Ai with K possible values for some K ≥ 2.”
Community detection in networks: A user guide (2016, methodology): “Other techniques to extract the number of groups have been proposed (Côme and Latouche, 2015; Daudin et al., 2008; Handcock et al., 2007; Latouche et al., 2012; Newman and Reinert, 2016).”