E. Airoldi, D. Blei, S. Fienberg, and E. Xing, Mixed-membership stochastic blockmodels, Journal of Machine Learning Research, vol.9, pp.1981-2014, 2008.

E. Allman, C. Matias, and J. Rhodes, Identifiability of parameters in latent structure models with many observed variables, The Annals of Statistics, vol.37, issue.6A, pp.3099-3132, 2009.
DOI : 10.1214/09-AOS689
URL : https://hal.archives-ouvertes.fr/hal-00591202

E. Allman, C. Matias, and J. Rhodes, Parameter identifiability in a class of random graph mixture models, Journal of Statistical Planning and Inference, vol.141, issue.5, pp.1719-1736, 2011.
DOI : 10.1016/j.jspi.2010.11.022
URL : https://hal.archives-ouvertes.fr/hal-00591197

C. Ambroise and C. Matias, New consistent and asymptotically normal parameter estimates for random-graph mixture models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.29, issue.1, pp.3-35, 2012.
DOI : 10.1111/j.1467-9868.2011.01009.x
URL : https://hal.archives-ouvertes.fr/hal-00647817

P. Bickel and A. Chen, A nonparametric view of network models and Newman???Girvan and other modularities, Proceedings of the National Academy of Sciences, vol.106, issue.50, pp.21068-21073, 2009.
DOI : 10.1073/pnas.0907096106

A. Celisse, J. Daudin, and L. Pierre, Consistency of maximum-likelihood and variational estimators in the stochastic block model, Electronic Journal of Statistics, vol.6, issue.0, pp.1847-1899, 2012.
DOI : 10.1214/12-EJS729
URL : https://hal.archives-ouvertes.fr/hal-00593644

A. Channarond, J. Daudin, and S. Robin, Classification and estimation in the Stochastic Blockmodel based on the empirical degrees, Electronic Journal of Statistics, vol.6, issue.0, pp.2574-2601, 2012.
DOI : 10.1214/12-EJS753
URL : https://hal.archives-ouvertes.fr/hal-01190224

D. S. Choi, P. J. Wolfe, and E. M. Airoldi, Stochastic blockmodels with a growing number of classes, Biometrika, vol.99, issue.2, pp.273-284, 2012.
DOI : 10.1093/biomet/asr053

J. Daudin, F. Picard, and S. Robin, A mixture model for random graphs, Statistics and Computing, vol.4, issue.2, pp.173-183, 2008.
DOI : 10.1007/s11222-007-9046-7
URL : https://hal.archives-ouvertes.fr/inria-00070186

W. Desarbo, D. Fong, J. Liechty, and M. K. Saxton, A hierarchical bayesian procedure for two-mode cluster analysis, Psychometrika, vol.22, issue.4, pp.547-572, 2004.
DOI : 10.1007/BF02289855

C. Flynn and P. Perry, Consistent biclustering

O. Frank and F. Harary, Cluster Inference by using Transitivity Indices in Empirical Graphs, Journal of the American Statistical Association, vol.39, issue.380, pp.835-840, 1982.
DOI : 10.1080/01621459.1973.10481342

S. Gazal, J. Daudin, and S. Robin, Accuracy of variational estimates for random graph mixture models, Journal of Statistical Computation and Simulation, vol.80, issue.5, pp.849-862, 2012.
DOI : 10.1038/ng881
URL : https://hal.archives-ouvertes.fr/inria-00494740

G. Govaert and M. Nadif, Clustering with block mixture models, Pattern Recognition, vol.36, issue.2, pp.463-473, 2003.
DOI : 10.1016/S0031-3203(02)00074-2

G. Govaert and M. Nadif, Block clustering with Bernoulli mixture models: Comparison of different approaches, Computational Statistics & Data Analysis, vol.52, issue.6, pp.3233-3245, 2008.
DOI : 10.1016/j.csda.2007.09.007

G. Govaert and M. Nadif, Latent Block Model for Contingency Table, Communications in Statistics - Theory and Methods, vol.24, issue.3, pp.416-425, 2010.
DOI : 10.1016/j.csda.2007.09.007
URL : https://hal.archives-ouvertes.fr/hal-00447792

J. A. Hartigan, Direct Clustering of a Data Matrix, Journal of the American Statistical Association, vol.27, issue.2, pp.123-129, 1972.
DOI : 10.1080/01621459.1963.10500845

P. Holland, K. Laskey, and S. Leinhardt, Stochastic blockmodels: First steps, Social Networks, vol.5, issue.2, pp.109-137, 1983.
DOI : 10.1016/0378-8733(83)90021-7

P. Latouche, E. Birmelé, and C. Ambroise, Overlapping stochastic block models with application to the French political blogosphere, The Annals of Applied Statistics, vol.5, issue.1, pp.309-336, 2011.
DOI : 10.1214/10-AOAS382SUPP
URL : https://hal.archives-ouvertes.fr/hal-00624538

P. Latouche, E. Birmelé, and C. Ambroise, Variational Bayesian inference and complexity control for stochastic block models, Statistical Modelling, vol.12, issue.1, pp.93-115, 2012.
DOI : 10.1177/1471082X1001200105
URL : https://hal.archives-ouvertes.fr/hal-00624536

M. Mariadassou, S. Robin, and C. Vacher, Uncovering latent structure in valued graphs: A variational approach, The Annals of Applied Statistics, vol.4, issue.2, pp.715-757, 2010.
DOI : 10.1214/07-AOAS361SUPP
URL : https://hal.archives-ouvertes.fr/hal-01197514

P. Massart, Concentration inequalities and model selection, Lecture Notes in Mathematics, vol.1896, 2007.

K. Nowicki and T. Snijders, Estimation and Prediction for Stochastic Blockstructures, Journal of the American Statistical Association, vol.96, issue.455, pp.1077-1087, 2001.
DOI : 10.1198/016214501753208735

F. Picard, V. Miele, J. Daudin, L. Cottret, and S. Robin, Deciphering the connectivity structure of biological networks using MixNet, BMC Bioinformatics, vol.10, issue.Suppl 6, pp.1-11, 2009.
DOI : 10.1186/1471-2105-10-S6-S17
URL : https://hal.archives-ouvertes.fr/hal-00428390

K. Rohe and B. Yu, Co-clustering for directed graphs: the stochastic coblockmodel and a spectral algorithm

K. Rohe, S. Chatterjee, and B. Yu, Spectral clustering and the high-dimensional stochastic blockmodel, The Annals of Statistics, vol.39, issue.4, pp.1878-1915, 2011.
DOI : 10.1214/11-AOS887

T. A. Snijders and K. Nowicki, Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure, Journal of Classification, vol.14, issue.1, pp.75-100, 1997.
DOI : 10.1007/s003579900004

J. Wyse and N. Friel, Block clustering with collapsed latent block models, Statistics and Computing, vol.28, issue.2, pp.415-428, 2012.
DOI : 10.1007/s11222-011-9233-4

H. Zanghi, C. Ambroise, and V. Miele, Fast online graph clustering via Erd??s???R??nyi mixture, Pattern Recognition, vol.41, issue.12, pp.3592-3599, 2008.
DOI : 10.1016/j.patcog.2008.06.019

H. Zanghi, F. Picard, V. Miele, and C. Ambroise, Strategies for online inference of model-based clustering in large and growing networks, The Annals of Applied Statistics, vol.4, issue.2, pp.687-714, 2010.
DOI : 10.1214/10-AOAS359
URL : https://hal.archives-ouvertes.fr/hal-00539318