C. Aicher, A. Jacobs, and A. Clauset, Adapting the stochastic block model to edge-weighted networks, ICML Workshop on Structured Learning (SLG), 2013.

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. M. Airoldi, T. B. Costa, and S. H. Chan, Stochastic blockmodel approximation of a graphon: Theory and consistent estimation, Advances in Neural Information Processing Systems 26, pp.692-700, 2013.

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

G. Ambroise, M. Grasseau, P. Hoebeke, V. Latouche, F. Miele et al., mixer: Routines for the analysis (unsupervised clustering) of networks using mixtures of Erdös-Rényi random graphs

A. A. Amini, A. Chen, P. J. Bickel, and E. Levina, Pseudo-likelihood methods for community detection in large sparse networks, The Annals of Statistics, vol.41, issue.4, pp.2097-2122, 2013.
DOI : 10.1214/13-AOS1138SUPP

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

P. Bickel, A. Chen, and E. Levina, The method of moments and degree distributions for network models, The Annals of Statistics, vol.39, issue.5, pp.2280-2301, 2011.
DOI : 10.1214/11-AOS904

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.7, pp.719-725, 2000.
DOI : 10.1109/34.865189

B. Bollobás, S. Janson, and O. Riordan, The phase transition in inhomogeneous random graphs, Random Structures and Algorithms, vol.123, issue.1, pp.3-122, 2007.
DOI : 10.1002/rsa.20168

O. Cappé, E. Moulines, and T. Rydén, Inference in hidden Markov models, 2005.

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, Recherche de structure dans un graphe aléatoire: modèlesmodèlesà espace latent

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

S. Chatterjee, Matrix estimation by Universal Singular Value Thresholding, The Annals of Statistics, vol.43, issue.1
DOI : 10.1214/14-AOS1272

D. S. Choi, P. J. Wolfe, and E. M. Airoldi, Stochastic blockmodels with a growing number of classes, Biometrika, vol.99, issue.2, pp.273-84, 2012.
DOI : 10.1093/biomet/asr053

F. Chung and L. Lu, The average distances in random graphs with given expected degrees, pp.15879-15882, 2002.

E. Côme and P. Latouche, Model selection and clustering in stochastic block models with the exact integrated complete data likelihood, 2013.

J. Daudin, A review of statistical models for clustering networks with an application to a PPI network, Journal de la Société Française de Statistique, pp.111-125, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01000028

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

J. Daudin, L. Pierre, and C. Vacher, Model for Heterogeneous Random Networks Using Continuous Latent Variables and an Application to a Tree-Fungus Network, Biometrics, vol.14, issue.4, pp.1043-1051, 2010.
DOI : 10.1111/j.1541-0420.2009.01378.x
URL : https://hal.archives-ouvertes.fr/hal-01197517

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. Ser. B, vol.39, issue.1, pp.1-38, 1977.

P. Diaconis and S. Janson, Graph limits and exchangeable random graphs, Rendiconti di Matematica, vol.28, pp.33-61, 2008.
DOI : 10.1080/15427951.2008.10129166
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2930250

C. Dubois, C. Butts, and P. Smyth, Stochastic blockmodeling of relational event dynamics, J. Machine Learning Res., Workshop & Conference Proc, vol.31, pp.238-284, 2013.

Y. Ephraim and N. Merhav, Hidden Markov processes, Special issue on Shannon theory: perspective, trends, and applications, pp.1518-1569, 2002.
DOI : 10.1109/TIT.2002.1003838

E. Erosheva, S. Fienberg, and J. Lafferty, Mixed-membership models of scientific publications, Proceedings of the National Academy of Sciences, vol.101, issue.Supplement 1, pp.11885-11892, 2004.
DOI : 10.1073/pnas.0307760101

S. Fortunato, Community detection in graphs, Physics Reports, vol.486, issue.3-5, pp.3-575, 2010.
DOI : 10.1016/j.physrep.2009.11.002

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

W. Fu, L. Song, and E. P. Xing, Dynamic mixed membership blockmodel for evolving networks, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.329-365, 2009.
DOI : 10.1145/1553374.1553416

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

A. Goldenberg, A. X. Zheng, S. E. Fienberg, and E. M. Airoldi, A Survey of Statistical Network Models, Foundations and Trends?? in Machine Learning, vol.2, issue.2, pp.129-233, 2010.
DOI : 10.1561/2200000005

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

A. Gunawardana and W. Byrne, Convergence theorems for generalized alternating minimization procedures, J. Mach. Learn. Res, vol.6, pp.2049-2073, 2005.

M. Handcock, A. Raftery, and J. Tantrum, Model-based clustering for social networks, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.6, issue.2, pp.301-54, 2007.
DOI : 10.1111/j.1467-9574.2005.00283.x

P. Hoff, Modeling homophily and stochastic equivalence in symmetric relational data, Advances in Neural Information Processing Systems, pp.657-664, 2007.

P. Hoff, A. Raftery, and M. Handcock, Latent Space Approaches to Social Network Analysis, Journal of the American Statistical Association, vol.97, issue.460, pp.1090-98, 2002.
DOI : 10.1198/016214502388618906

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

T. S. Jaakkola, Tutorial on variational approximation methods In In Advanced Mean Field Methods: Theory and Practice, pp.129-159, 2000.

Y. Jernite, P. Latouche, C. Bouveyron, P. Rivera, L. Jegou et al., The random subgraph model for the analysis of an ecclesiastical network in Merovingian Gaul, The Annals of Applied Statistics, vol.8, issue.1, pp.377-405, 2014.
DOI : 10.1214/13-AOAS691SUPP
URL : https://hal.archives-ouvertes.fr/hal-00764160

Q. Jiang, Y. Zhang, and M. Sun, Community Detection on Weighted Networks: A Variational Bayesian Method, Machine Learning, pp.176-190, 2009.
DOI : 10.1007/978-3-642-05224-8_15

B. Karrer and M. E. Newman, Stochastic blockmodels and community structure in networks, Physical Review E, vol.83, issue.1, p.16107, 2011.
DOI : 10.1103/PhysRevE.83.016107

C. Keribin, V. Brault, G. Celeux, and G. Govaert, Model selection for the binary latent block model, 20th International Conference on Computational Statistics, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00924210

E. D. Kolaczyk, Statistical Analysis of Network Data: Methods and Models, 2009.

J. D. Lafferty, A. Mccallum, and F. C. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, Proceedings of the Eighteenth International Conference on Machine Learning, ICML '01, pp.282-289, 2001.

P. Latouche, Modèles de graphes aléatoiresaléatoiresà structure cachée pour l'analyse des réseaux, 2010.

P. Latouche and S. Robin, Bayesian model averaging of stochastic block models to estimate the graphon function and motif frequencies in a W-graph model, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00876334

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

P. Latouche, E. Birmelé, and C. Ambroise, OSBM: Overlapping stochastic blockmodel, 2012b. R package

S. L. Lauritzen, Graphical models, volume 17 of Oxford Statistical Science Series, 1996.

J. Leger, Wmixnet: Software for clustering the nodes of binary and valued graphs using the stochastic block model, 2014.

J. Leger, C. Vacher, and J. Daudin, Detection of structurally homogeneous subsets in graphs, Statistics and Computing, vol.33, issue.4, pp.1-18, 2013.
DOI : 10.1007/s11222-013-9395-3
URL : https://hal.archives-ouvertes.fr/hal-01019247

L. Lovász and B. Szegedy, Limits of dense graph sequences, Journal of Combinatorial Theory, Series B, vol.96, issue.6, pp.933-957, 2006.
DOI : 10.1016/j.jctb.2006.05.002

L. Lovász and B. Szegedy, Szemer??di???s Lemma for the Analyst, GAFA Geometric And Functional Analysis, vol.17, issue.1, pp.252-270, 2007.
DOI : 10.1007/s00039-007-0599-6

M. Mariadassou and C. Matias, Convergence of the groups posterior distribution in latent or stochastic block models, Bernoulli, vol.21, issue.1
DOI : 10.3150/13-BEJ579
URL : https://hal.archives-ouvertes.fr/hal-01174515

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

V. Miele, F. Picard, and S. Dray, Spatially constrained clustering of ecological networks, Methods in Ecology and Evolution, vol.30, issue.Suppl 6, pp.771-779, 2014.
DOI : 10.1111/2041-210X.12208

M. Mørup and L. K. Hansen, Learning latent structure in complex networks, NIPS Workshop on Analyzing Networks and Learning with Graphs, 2009.

M. E. Newman, S. H. Strogatz, and D. J. Watts, Random graphs with arbitrary degree distributions and their applications, Physical Review E, vol.64, issue.2, p.26118, 2001.
DOI : 10.1103/PhysRevE.64.026118

C. Nickel, Random dot product graphs: A model for social networks, 2006.

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

S. C. Olhede and P. J. Wolfe, Network histograms and universality of blockmodel approximation, Proceedings of the National Academy of Sciences, vol.111, issue.41, pp.1312-5306, 2013.
DOI : 10.1073/pnas.1400374111

J. Park and M. Newman, Origin of degree correlations in the Internet and other networks, Physical Review E, vol.68, issue.2, p.26112, 2003.
DOI : 10.1103/PhysRevE.68.026112

P. E. Pattison and G. L. Robins, Handbook of Probability Theory with Applications, chapter Probabilistic Network Theory, 2007.

T. P. Peixoto, Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models, Physical Review E, vol.89, issue.1, p.12804, 2014.
DOI : 10.1103/PhysRevE.89.012804

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

J. Reichardt, R. Alamino, and D. Saad, The Interplay between Microscopic and Mesoscopic Structures in Complex Networks, PLoS ONE, vol.2, issue.2009, p.21282, 2011.
DOI : 10.1371/journal.pone.0021282.s005

G. Robins, P. Pattison, Y. Kalish, and D. Lusher, An introduction to exponential random graph (p*) models for social networks, Social Networks, vol.29, issue.2, pp.173-191, 2007.
DOI : 10.1016/j.socnet.2006.08.002

K. Rohe and B. Yu, Co-clustering for directed graphs: the stochastic co-blockmodel and a spectral algorithm, 2012.

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, Statistical Models for Social Networks, Annual Review of Sociology, vol.37, issue.1, pp.129-151, 2011.
DOI : 10.1146/annurev.soc.012809.102709

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

D. Sussman, M. Tang, and C. Priebe, Consistent latent position estimation and vertex classification for random dot product graphs. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.36, issue.1, pp.48-57, 2014.

C. Sutton and A. Mccallum, An Introduction to Conditional Random Fields, Machine Learning, p.2012
DOI : 10.1561/2200000013

E. Szemerédi, Regular partitions of graphs InProbì emes combinatoires et théorie des graphes, Colloq. Internat. CNRS, vol.260, pp.399-401, 1976.

C. Tallberg, A BAYESIAN APPROACH TO MODELING STOCHASTIC BLOCKSTRUCTURES WITH COVARIATES, The Journal of Mathematical Sociology, vol.99, issue.1, pp.1-23, 2005.
DOI : 10.2307/2289140

M. Tang, D. Sussman, and C. Priebe, Universally consistent vertex classification for latent positions graphs, The Annals of Statistics, vol.41, issue.3, pp.1406-1430, 2013.
DOI : 10.1214/13-AOS1112

D. Vu, D. Hunter, and M. Schweinberger, Model-based clustering of large networks, The Annals of Applied Statistics, vol.7, issue.2, pp.613-1248, 2013.
DOI : 10.1214/12-AOAS617

P. J. Wolfe and S. C. Olhede, Nonparametric graphon estimation, 2013.

K. S. Xu and A. Hero, Dynamic Stochastic Blockmodels: Statistical Models for Time-Evolving Networks, Social Computing, pp.201-210, 2013.
DOI : 10.1007/978-3-642-37210-0_22

X. Yan, C. Shalizi, J. E. Jensen, F. Krzakala, C. Moore et al., Model selection for degree-corrected block models, Journal of Statistical Mechanics: Theory and Experiment, vol.2014, issue.5, pp.2014-05007, 2014.
DOI : 10.1088/1742-5468/2014/05/P05007

S. J. Young and E. R. Scheinerman, Random Dot Product Graph Models for Social Networks, Algorithms and Models for the Web-Graph, pp.138-149, 2007.
DOI : 10.1007/978-3-540-77004-6_11

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

H. Zanghi, S. Volant, and C. Ambroise, Clustering based on random graph model embedding vertex features, Pattern Recognition Letters, vol.31, issue.9, pp.830-836, 2010.
DOI : 10.1016/j.patrec.2010.01.026

Y. Zhao, E. Levina, and J. Zhu, Consistency of community detection in networks under degree-corrected stochastic block models, The Annals of Statistics, vol.40, issue.4, pp.2266-2292, 2012.
DOI : 10.1214/12-AOS1036SUPP

Y. Zhu, X. Yan, and C. Moore, Oriented and degree-generated block models: generating and inferring communities with inhomogeneous degree distributions, Journal of Complex Networks, vol.2, issue.1, 2013.
DOI : 10.1093/comnet/cnt011