Likelihood-based model selection for stochastic block models

Keywords

Stochastic block model, Overfitting, Model selection, Mathematics, Statistic

References

• Estimating the number of communities in networks by spectral methodsCan M. Le, Elizaveta Levina — 2015
• On Consistency of Community Detection in NetworksYunpeng Zhao, Elizaveta Levina, Ji Zhu — 2011
• Nonparametric graphon estimationPatrick J. Wolfe, Sofia C. Olhede — 2013
• How Many Communities Are There?Diego Franco Saldana, Yi Yu, Yang Feng — 2014
• Network Cross-Validation for Determining the Number of Communities in Network DataKehui Chen, Jing Lei — 2014
• A goodness-of-fit test for stochastic block modelsJing Lei — 2014
• Consistency of community detection in networks under degree-corrected stochastic block modelsYunpeng Zhao, Elizaveta Levina, Ji Zhu — 2011
• Spectral clustering and the high-dimensional stochastic blockmodelKarl Rohe, Sourav Chatterjee, Bin Yu — 2010
• An efficient and principled method for detecting communities in networksB. Ball, Brian Karrer, M. E. J. Newman — 2011
• Pseudo-likelihood methods for community detection in large sparse networksArash A. Amini, Aiyou Chen, Peter J. Bickel — 2012
• Asymptotic Normality of Maximum Likelihood and its Variational Approximation for Stochastic BlockmodelsPeter J. Bickel, David Choi, Xiangyu Chang — 2012
• Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applicationsAurélien Decelle, Florent Krząkała, Cristopher Moore — 2011
• Finding community structure in networks using the eigenvectors of matrices.M. E. J. Newman — 2006
• Parsimonious module inference in large networks.Tiago P. Peixoto — 2012
• Impact of regularization on spectral clusteringAntony Joseph, Bin Yu — 2013
• Consistency of maximum-likelihood and variational estimators in the Stochastic Block ModelAlain Célisse, Jean-Jacques Daudin, L. Pierre — 2011
• Consistent Adjacency-Spectral Partitioning for the Stochastic Block Model When the Model Parameters Are UnknownDonniell E. Fishkind, Daniel L. Sussman, Minh Tang — 2012
• A nonparametric view of network models and Newman–Girvan and other modularitiesPeter J. Bickel, Aiyou Chen — 2009
• Stochastic blockmodels: First stepsPaul W. Holland, Kathryn Blackmond Laskey, Samuel Leinhardt — 1983
• Mixed Membership Stochastic BlockmodelsEdoardo M. Airoldi, David M. Blei, Stephen E. Fienberg — 2007

Cited by

• Estimating the number of communities in networks by spectral methodsCan M. Le, Elizaveta Levina — 2015
• A Model Selection Approach for Clustering a Multinomial Sequence with Non-Negative FactorizationNam H. Lee, Runze Tang, Carey E. Priebe — 2013
• Network Cross-Validation for Determining the Number of Communities in Network DataKehui Chen, Jing Lei — 2014
• Problems in Network Modeling: Estimating Edges and Community DetectionYing Xiang Wang — 2015
• Bayesian model selection of stochastic block modelsXiaoran Yan — 2016
• Bayesian Community DetectionStéphanie van der Pas, Aad van der Vaart — 2016
• Community detection with nodal information: Likelihood and its variational approximationHaolei Weng, Yang Feng — 2016
• On consistency of model selection for stochastic block modelsJianwei Hu, Hong Qin, Ting Yan — 2016
• A Bayesian analysis of weighted stochastic block models with applications in brain functional connectomicsChristopher Bryant — 2016
• Estimating Community Structure in Networks by Spectral MethodsCan M. Le — 2016
• Generalizations and Applications of the Stochastic Block Model to Basketball Games and Variable Selection ProblemsLu Xin — 2017
• Understanding Community Structure for Large NetworksB. Franke — 2016
• Using Maximum Entry-Wise Deviation to Test the Goodness of Fit for Stochastic Block ModelsJianwei Hu, Jingfei Zhang, Hong Qin — 2017
• Massive-scale estimation of exponential-family random graph models with local dependenceSergii Babkin, Michael Schweinberger — 2017
• Regular Decomposition: an information and graph theoretic approach to stochastic block modelsHannu Reittu, Fülöp Bazsó, Ilkka Norros — 2017
• Testing Network Structure Using Relations Between Small Subgraph ProbabilitiesChao Gao, John Lafferty — 2017
• Optimal hypothesis testing for stochastic block models with growing degreesDebapratim Banerjee, Zongming Ma — 2017
• A survey on theoretical advances of community detection in networksYunpeng Zhao — 2017
• Community Detection by L_0-penalized Graph LaplacianChong Chen, Ruibin Xi, Nan Lin — 2017
• Strong Consistency of Spectral Clustering for Stochastic Block ModelsLiangjun Su, Wuyi Wang, Yichong Zhang — 2017

Related papers

• Stochastic blockmodels and community structure in networksBrian Karrer, M. E. J. Newman — 2010
• Stochastic blockmodels: First stepsPaul W. Holland, Kathryn Blackmond Laskey, Samuel Leinhardt — 1983
• A nonparametric view of network models and Newman–Girvan and other modularitiesPeter J. Bickel, Aiyou Chen — 2009
• Empirical Likelihood for Partially Linear ModelsJian Shi, Tai-Shing Lau — 2000
• Consistent model selection based on parameter estimatesWenxin Jiang, Xiangyang Liu — 2004
• Classification and estimation in the Stochastic Block Model based on the empirical degreesAntoine Channarond, Jean‐Jacques Daudin, Stéphane Robin — 2011
• Adjusted Empirical Likelihood and its PropertiesJiahua Chen, Asokan Mulayath Variyath, Bovas Abraham — 2008