A. Ahmed and E. P. Xing, On tight approximate inference of logistic-normal admixture model, Proceedings of the International Conference on Artificial Intelligence and Statistics, pp.1-8, 2007.

E. M. Airoldi, D. M. Blei, S. E. Fienberg, E. P. Xing, and T. Jaakkola, Mixed membership stochastic block models for relational data with application to protein-protein interactions, Proceedings of the international biometrics society annual meeting, pp.1-34, 2006.

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

H. Akaike, Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory, pp.267-281, 1973.

H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

R. Albert and A. L. Barabási, Statistical mechanics of complex networks. Modern Physics, pp.47-97, 2002.

R. Albert, H. Jeong, and A. L. Barabasi, Diameter of the world-wide web, Nature, vol.401, pp.130-131, 1999.

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

C. Ambroise, G. Grasseau, M. Hoebeke, P. Latouche, V. Miele et al., The mixer R package (version 1, 2010.

A. L. Barabasi and R. Albert, Emergence of scaling in random networks, Science, vol.286, pp.509-512, 1999.

A. L. Barabási and Z. N. Oltvai, Network biology: understanding the cell's functional organization, Nature Reviews Genetics, vol.5, issue.2, pp.101-113, 2004.
DOI : 10.1038/nrg1272

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

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

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

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.41-44, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

J. A. Bilmes, A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models, International Computer Science Institute, vol.4, p.126, 1998.

C. M. Bishop, Pattern recognition and machine learning, 2006.

D. Blei and J. Lafferty, A correlated topic model of Science, The Annals of Applied Statistics, vol.1, issue.1, pp.17-35, 2007.
DOI : 10.1214/07-AOAS114

D. M. Blei and J. D. Lafferty, Dynamic topic models, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.113-120, 2006.
DOI : 10.1145/1143844.1143859

D. M. Blei and J. D. Lafferty, A correlated topic model of science. The Annals of Applied Statistics, pp.17-35, 2007.

V. D. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, vol.2008, issue.10, pp.10008-10020, 2008.
DOI : 10.1088/1742-5468/2008/10/P10008

URL : https://hal.archives-ouvertes.fr/hal-01146070

C. Bouveyron, Y. Jernite, P. Latouche, and L. Nouedoui, The rambo R package (version 1.1), 2013. http://cran.r-project

C. Bouveyron, P. Latouche, and R. Zreik, The stochastic topic block model for the clustering of vertices in networks with textual edges, Statistics and Computing, vol.31, issue.9, p.page DOI, 2016.
DOI : 10.1177/1471082X15577017

URL : https://hal.archives-ouvertes.fr/hal-01299161

A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan et al., Graph structure in the Web, Computer Networks, vol.33, issue.1-6, pp.309-320, 2000.
DOI : 10.1016/S1389-1286(00)00083-9

G. Celeux and G. Govaert, A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, pp.73-82, 1991.
DOI : 10.1016/0167-9473(92)90042-E

URL : https://hal.archives-ouvertes.fr/inria-00075196

J. Chang and D. M. Blei, Relational topic models for document networks, International Conference on Artificial Intelligence and Statistics, pp.81-88, 2009.

E. Côme and P. Latouche, Model selection and clustering in stochastic block models with the exact integrated complete data likelihood, Statistical Modelling, pp.10-1177, 2015.

E. Côme, N. A. Randriamanamihaga, L. Oukhellou, and P. Aknin, Spatiotemporal analysis of dynamic origin-destination data using latent dirichlet allocation: Application to vélib'bike sharing system of paris, TRB 93rd Annual meeting, p.19, 2014.

A. Corduneanu and C. M. Bishop, Variational bayesian model selection for mixture distributions, Artificial intelligence and Statistics, pp.27-34, 2001.

A. Dasgupta and A. E. Raftery, Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering, Journal of the American Statistical Association, vol.26, issue.441, pp.294-302, 1998.
DOI : 10.1080/01621459.1986.10478240

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

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological), pp.1-38, 1977.

S. N. Dorogovtsev, J. F. Mendes, and A. N. Samukhin, Structure of Growing Networks with Preferential Linking, Physical Review Letters, vol.85, issue.21, pp.4633-4636, 2000.
DOI : 10.1103/PhysRevLett.85.4633

C. Dubois, C. T. Butts, and P. Smyth, Stochastic blockmodelling of relational event dynamics, International Conference on Artificial Intelligence and Statistics, volume 31 of the Journal of Machine Learning Research Proceedings, pp.238-246, 2013.

C. Ducruet, Network diversity and maritime flows, Journal of Transport Geography, vol.30, pp.77-88, 2013.
DOI : 10.1016/j.jtrangeo.2013.03.004

URL : https://hal.archives-ouvertes.fr/halshs-00815731

P. Erdös and A. Rényi, On random graphs, i, Publicationes Mathematicae (Debrecen), vol.6, pp.290-297, 1959.

S. E. Fienberg and S. Wasserman, Categorical Data Analysis of Single Sociometric Relations, Sociological Methodology, vol.12, pp.156-192, 1981.
DOI : 10.2307/270741

S. E. Fienberg and S. S. Wasserman, Categorical Data Analysis of Single Sociometric Relations, Sociological Methodology, vol.12, pp.156-192, 1981.
DOI : 10.2307/270741

J. R. Foulds, C. Dubois, A. U. Asuncion, C. T. Butts, and P. Smyth, A dynamic relational infinite feature model for longitudinal social networks, International Conference on Artificial Intelligence and Statistics, pp.287-295, 2011.

L. C. Freeman, Some antecedents of social network analysis, Connections, vol.19, issue.1, pp.39-42, 1996.

A. E. Gelfand and A. Smith, Sampling-Based Approaches to Calculating Marginal Densities, Journal of the American Statistical Association, vol.4, issue.410, pp.398-409, 1990.
DOI : 10.1080/01621459.1986.10478240

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.512.2330

S. Geman and D. Geman, Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, IEEE Transactions on pattern analysis and machine intelligence, issue.6, pp.721-741, 1984.

Z. Ghahramani and T. L. Griffiths, Infinite latent feature models and the indian buffet process, Advances in neural information processing systems, pp.475-482, 2005.

M. Girvan and M. E. Newman, Community structure in social and biological networks, Proceedings of the National Academy of Sciences, p.7821, 2002.
DOI : 10.1073/pnas.122653799

D. Greene, D. Doyle, and P. Cunningham, Tracking the Evolution of Communities in Dynamic Social Networks, 2010 International Conference on Advances in Social Networks Analysis and Mining, pp.176-183, 2010.
DOI : 10.1109/ASONAM.2010.17

B. Grun and K. Hornik, The mixer topicmodels package (version 0.2-3), 2013

M. S. Handcock, A. E. Raftery, and J. M. Tantrum, Model-based clustering for social networks, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.6, issue.2, pp.301-354, 2007.
DOI : 10.1111/j.1467-9574.2005.00283.x

A. C. Harvey, Forecasting, structural time series models and the Kalman filter, 1989.

R. J. Hathaway, Another interpretation of the EM algorithm for mixture distributions, Statistics & Probability Letters, vol.4, issue.2, pp.53-56, 1986.
DOI : 10.1016/0167-7152(86)90016-7

C. Heaukulani and Z. Ghahramani, Dynamic probabilistic models for latent feature propagation in social networks, Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp.275-283, 2013.

Q. Ho, L. Song, and E. P. Xing, Evolving cluster mixed-membership blockmodel for time-evolving networks, International Conference on Artificial Intelligence and Statistics, pp.342-350, 2011.

P. D. Hoff, A. E. Raftery, and M. S. Handcock, Latent Space Approaches to Social Network Analysis, Journal of the American Statistical Association, vol.97, issue.460, pp.971090-1098, 2002.
DOI : 10.1198/016214502388618906

J. M. Hofman and C. H. Wiggins, Bayesian approach to network modularity. Physical review letters, p.258701, 2008.
DOI : 10.1103/physrevlett.100.258701

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724184

T. Hofmann, Probabilistic latent semantic indexing, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '99, pp.50-57, 1999.
DOI : 10.1145/312624.312649

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.4458

E. E. Holmes, E. J. Ward, and K. Wills, Marss: Multivariate autoregressive state-space models for analyzing time-series data, The R Journal, vol.4, issue.1, pp.11-19, 2012.

T. S. Jaakkola, 10 tutorial on variational approximation methods Advanced mean field methods: theory and practice, p.129, 2001.

K. Anil, . Jain, C. Richard, and . Dubes, Algorithms for clustering data, 1988.

Y. Jernite, P. Latouche, C. Bouveyron, P. Rivera, L. Jegou et al., The random subgraph model for the analysis of an acclesiastical network in merovingian gaul, Annals of Applied Statistics, vol.8, issue.1, pp.55-74, 2014.

M. Jordan, Z. Ghahramani, T. Jaakkola, and L. K. Saul, An Introduction to Variational Methods for Graphical Models, Machine learning, vol.37, issue.2, pp.183-233, 1999.
DOI : 10.1007/978-94-011-5014-9_5

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.3844

R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960.
DOI : 10.1115/1.3662552

R. E. Kass and A. E. Raftery, Bayes Factors, Journal of the American Statistical Association, vol.2, issue.430, pp.773-795, 1995.
DOI : 10.1080/01621459.1995.10476572

L. Kaufman, J. Peter, and . Rousseeuw, Partitioning around medoids (program pam) Finding groups in data: an introduction to cluster analysis, pp.68-125, 1990.

C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada, and N. Ueda, Learning systems of concepts with an infinite relational model, Proceedings of the National Conference on Artificial Intelligence, pp.381-391, 2006.

M. Kim and J. Leskovec, Nonparametric multi-group membership model for dynamic networks, Advances in Neural Information Processing Systems (25), pp.1385-1393, 2013.

M. Kim and J. Leskovec, Nonparametric multi-group membership model for dynamic networks, Advances in neural information processing systems, pp.1385-1393, 2013.

T. Krishnan and G. J. Mclachlan, The EM algorithm and extensions, 1997.

P. N. Krivitsky and M. Handcock, Fitting position latent cluster models for social networks with latentnet, 2008.
DOI : 10.18637/jss.v024.i05

URL : http://doi.org/10.18637/jss.v024.i05

L. Labiod and Y. Bennani, A spectral based clustering algorith for categorical data with maximum modularity, ESANN 2011 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 2011.
DOI : 10.1109/icdm.2011.37

V. Lacroix, C. G. Fernandes, and M. Sagot, Motif Search in Graphs: Application to Metabolic Networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.3, issue.4, pp.360-368, 2006.
DOI : 10.1109/TCBB.2006.55

URL : https://hal.archives-ouvertes.fr/hal-00427984

J. D. Lafferty and D. M. Blei, Correlated topic models, Advances in Neural Information Processing Systems 18, pp.147-154, 2006.

P. Latouche, Modèles de graphes aléatoires à structure cachée pour l'analyse des réseaux, 2011.

P. Latouche, C. Birmelé, and . 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, C. Birmelé, and . 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, C. Birmelé, and . Ambroise, Model selection in overlapping stochastic block models, Electronic Journal of Statistics, vol.8, issue.1, pp.762-794, 2014.
DOI : 10.1214/14-EJS903

URL : https://hal.archives-ouvertes.fr/hal-00990277

P. Latouche, R. Zreik, and C. Bouveyron, Cluster identification in maritime flows with stochastic methods. Maritime Networks: Spatial Structures and Time Dynamics, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01199613

S. Lazebnik, C. Schmid, and J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.2169-2178, 2006.
DOI : 10.1109/CVPR.2006.68

URL : https://hal.archives-ouvertes.fr/inria-00548585

B. G. Leroux, Consistent Estimation of a Mixing Distribution, The Annals of Statistics, vol.20, issue.3, pp.1350-1360, 1992.
DOI : 10.1214/aos/1176348772

Y. Liu, A. Niculescu-mizil, and W. Gryc, Topic-link LDA, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.665-672, 2009.
DOI : 10.1145/1553374.1553460

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp.281-297, 1967.

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.715-742, 2010.
DOI : 10.1214/07-AOAS361SUPP

URL : https://hal.archives-ouvertes.fr/hal-01197514

C. Matias and V. Miele, Statistical clustering of temporal networks through a dynamic stochastic block model, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.4, 2016.
DOI : 10.1111/rssb.12200

URL : https://hal.archives-ouvertes.fr/hal-01167837

C. Matias and S. Robin, Modeling heterogeneity in random graphs through latent space models: a selective review, Esaim Proc. and Surveys, pp.55-74, 2014.
DOI : 10.1051/proc/201447004

URL : https://hal.archives-ouvertes.fr/hal-00948421

A. Mc-daid, T. B. Murphy, N. Frieln, and N. J. Hurley, Improved Bayesian inference for the stochastic block model with application to large networks, Computational Statistics & Data Analysis, vol.60, pp.12-31, 2013.
DOI : 10.1016/j.csda.2012.10.021

A. Mccallum, A. Corrada-emmanuel, and X. Wang, The author-recipient-topic model for topic and role discovery in social networks, with application to enron and academic email, Workshop on Link Analysis, Counterterrorism and Security, pp.33-44, 2005.

G. Mclachlan and D. Peel, Finite mixture models, 2004.
DOI : 10.1002/0471721182

G. J. Mclachlan and K. E. Basford, Mixture models. inference and applications to clustering. Statistics: Textbooks and Monographs, 1988.

M. Mcpherson, L. Smith-lovin, and J. M. Cook, Birds of a feather: Homophily in social networks. Annual review of sociology, pp.415-444, 2001.

T. P. Minka, From hidden markov models to linear dynamical systems, 1998.

T. Minka, From hidden markov models to linear dynamical systems, 1999.

J. L. Moreno, Who shall survive?: A new approach to the problem of human interrelations, 1934.
DOI : 10.1037/10648-000

R. Neal and G. E. Hinton, A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants, Learning in graphical models, pp.355-368, 1998.
DOI : 10.1007/978-94-011-5014-9_12

D. Newman, P. Smyth, M. Welling, and A. U. Asuncion, Distributed inference for latent dirichlet allocation, Advances in neural information processing systems, pp.1081-1088, 2007.

M. E. Newman, Fast algorithm for detecting community structure in networks, Physical Review E, vol.69, issue.6, p.66133, 2004.
DOI : 10.1103/PhysRevE.69.066133

M. E. Newman and M. Girvan, Finding and evaluating community structure in networks, Physical Review E, vol.69, issue.2, p.26113, 2004.
DOI : 10.1103/PhysRevE.69.026113

K. Nigam, A. Mccallum, S. Thrun, and T. Mitchell, Text classification from labeled and unlabeled documents using em, Machine Learning, vol.39, issue.2/3, pp.103-134, 2000.
DOI : 10.1023/A:1007692713085

K. Nowicki and T. A. 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

G. Palla, I. Derenyi, I. Farkas, and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature, vol.387, issue.7043, pp.814-818, 2005.
DOI : 10.1038/nature03248

C. Papadimitriou, P. Raghavan, H. Tamaki, and S. Vempala, Latent semantic indexing, Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems , PODS '98, pp.159-168, 1998.
DOI : 10.1145/275487.275505

N. Pathak, C. Delong, A. Banerjee, and K. Erickson, Social topic models for community extraction, The 2nd SNA-KDD workshop, 2008.

D. Peel and G. J. Mclachlan, Robust mixture modelling using the t distribution, Statistics and Computing, vol.10, issue.4, pp.339-348, 2000.
DOI : 10.1023/A:1008981510081

L. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition, Proceedings of the IEEE, pp.257-286, 1989.

W. M. Rand, Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, vol.15, issue.336, pp.846-850, 1971.
DOI : 10.1080/01621459.1963.10500845

H. E. Rauch, C. T. Striebel, and F. Tung, Maximum likelihood estimates of linear dynamic systems, AIAA Journal, vol.3, issue.8, pp.1445-1450, 1965.
DOI : 10.2514/3.3166

H. E. Rauch, F. Tung, and T. Striebel, Maximum likelihood estimates of linear dynamic systems, AIAA Journal, vol.3, issue.8, pp.1445-1450, 1965.
DOI : 10.2514/3.3166

M. Rosen-zvi, T. Griffiths, M. Steyvers, and P. Smyth, The author-topic model for authors and documents, Proceedings of the 20th conference on Uncertainty in artificial intelligence, pp.487-494, 2004.

F. Rossi, N. Villa-vialaneix, and F. Hautefeuille, Exploration of a large database of French notarial acts with social network methods, Digital Medievalist, vol.9, pp.1-20, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01053673

B. C. Russell, W. T. Freeman, A. A. Efros, J. Sivic, and A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.1605-1614, 2006.
DOI : 10.1109/CVPR.2006.326

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.184.2856

M. Sachan, D. Contractor, T. Faruquie, and L. Subramaniam, Using content and interactions for discovering communities in social networks, Proceedings of the 21st international conference on World Wide Web, WWW '12, pp.331-340, 2012.
DOI : 10.1145/2187836.2187882

M. Salter-townshend and T. B. Murphy, Variational bayesian inference for the latent position cluster model, Analyzing Networks and Learning with Graphs Workshop at 23rd annual conference on Neural Information Processing Systems Whister, 2009.
DOI : 10.1016/j.csda.2012.08.004

P. Sarkar and A. W. Moore, Dynamic social network analysis using latent space models, ACM SIGKDD Explorations Newsletter, vol.7, issue.2, pp.31-40, 2005.
DOI : 10.1145/1117454.1117459

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.332.1164

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

G. Schwarz, Estimating the dimension of a model. The annals of statistics, pp.461-464, 1978.

M. Steyvers, P. Smyth, M. Rosen-zvi, and T. Griffiths, Probabilistic authortopic models for information discovery, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.306-315, 2004.
DOI : 10.1145/1014052.1014087

Y. Sun, J. Han, J. Gao, and Y. Yu, iTopicModel: Information Network-Integrated Topic Modeling, 2009 Ninth IEEE International Conference on Data Mining, pp.493-502, 2009.
DOI : 10.1109/ICDM.2009.43

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.215.7699

M. Svensén and C. M. Bishop, Robust Bayesian mixture modelling, Neurocomputing, vol.64, pp.235-252, 2004.
DOI : 10.1016/j.neucom.2004.11.018

M. Svensén and C. M. Bishop, Robust Bayesian mixture modelling, Neurocomputing, vol.64, pp.235-252, 2005.
DOI : 10.1016/j.neucom.2004.11.018

Y. W. Teh, D. Newman, and M. Welling, A collapsed variational bayesian inference algorithm for latent Dirichlet allocation Advances in neural information processing systems, pp.1353-1360, 2006.

Y. J. Wang and G. Y. Wong, Stochastic Blockmodels for Directed Graphs, Journal of the American Statistical Association, vol.4, issue.397, pp.8-19, 1987.
DOI : 10.1080/01621459.1987.10478406

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.322

H. C. White, S. A. Boorman, and R. L. Breiger, Social Structure from Multiple Networks. I. Blockmodels of Roles and Positions, American Journal of Sociology, vol.81, issue.4, pp.730-780, 1976.
DOI : 10.1086/226141

E. P. Xing, W. Fu, and L. Song, A state-space mixed membership blockmodel for dynamic network tomography, The Annals of Applied Statistics, vol.4, issue.2, pp.535-566, 2010.
DOI : 10.1214/09-AOAS311

K. S. Xu, Stochastic block transition models for dynamic networks, International Conference on Artificial Intelligence and Statistics, pp.1079-1087, 2015.

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

URL : http://arxiv.org/abs/1304.5974

T. Yang, Y. Chi, S. Zhu, Y. Gong, and R. Jin, Detecting communities and their evolutions in dynamic social networks???a??Bayesian approach, Machine Learning, vol.2, issue.1, pp.157-189, 2011.
DOI : 10.1007/s10994-010-5214-7

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, 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

URL : http://arxiv.org/abs/0910.2107

D. Zhou, E. Manavoglu, J. Li, C. Giles, and H. Zha, Probabilistic models for discovering e-communities, Proceedings of the 15th international conference on World Wide Web , WWW '06, pp.173-182, 2006.
DOI : 10.1145/1135777.1135807

R. Zreik, P. Latouche, and C. Bouveyron, Classification automatique de réseaux dynamiques avec sous-graphes: étude du scandale enron, Journal de la Société Française de Statistique, pp.166-191, 2015.

R. Zreik, P. Latouche, and C. Bouveyron, The dynamic random subgraph model for the clustering of evolving networks, Computational Statistics, vol.31, issue.9, 2016.
DOI : 10.1007/s00180-016-0655-5

URL : https://hal.archives-ouvertes.fr/hal-01122393