Mixed-membership stochastic blockmodels, Journal of Machine Learning Research, vol.9, pp.1981-2014, 2008. ,
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
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
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
Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels, The Annals of Statistics, vol.41, issue.4, pp.1922-1943, 2013. ,
DOI : 10.1214/13-AOS1124
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
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
Detecting rich-club ordering in complex networks, Nature Physics, vol.65, issue.2, pp.110-115, 2006. ,
DOI : 10.1038/nphys209
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
Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. Ser. B, vol.39, issue.1, pp.1-38, 1977. ,
Community detection in graphs, Physics Reports, vol.486, issue.3-5, pp.75-174, 2010. ,
DOI : 10.1016/j.physrep.2009.11.002
Contact Patterns among High School Students, PLoS ONE, vol.59, issue.9, p.107878, 2014. ,
DOI : 10.1371/journal.pone.0107878.s001
URL : https://hal.archives-ouvertes.fr/hal-01065922
A Survey of Statistical Network Models, Foundations and Trends?? in Machine Learning, vol.2, issue.2, pp.129-233, 2010. ,
DOI : 10.1561/2200000005
Convergence theorems for generalized alternating minimization procedures, J. Mach. Learn. Res, vol.6, pp.2049-2073, 2005. ,
Dynamic probabilistic models for latent feature propagation in social networks, Proceedings of the 30th International Conference on Machine Learning (ICML-13) Conference Proceedings, pp.275-283, 2013. ,
Modeling temporal evolution and multiscale structure in networks, Proceedings of the 30th International Conference on Machine Learning (ICML-13) Conference Proceedings, pp.960-968, 2013. ,
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
Modern temporal network theory: a colloquium, The European Physical Journal B, vol.446, issue.9, p.234, 2015. ,
DOI : 10.1140/epjb/e2015-60657-4
Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985. ,
DOI : 10.1007/BF01908075
Dynamic infinite relational model for time-varying relational data analysis, Advances in Neural Information Processing Systems 23, pp.919-927, 2010. ,
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
Statistical Analysis of Network Data: Methods and Models, 2009. ,
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
Maximum-likelihood estimation for hidden Markov models. Stochastic Process, Appl, vol.40, issue.1, pp.127-143, 1992. ,
Persistent Community Detection in Dynamic Social Networks, Advances in Knowledge Discovery and Data Mining, pp.78-89, 2014. ,
DOI : 10.1007/978-3-319-06608-0_7
Modeling heterogeneity in random graphs through latent space models: a selective review, ESAIM: Proc. 47, pp.55-74, 2014. ,
DOI : 10.1051/proc/201447004
URL : https://hal.archives-ouvertes.fr/hal-00948421
Reconstruction and estimation in the planted partition model, Probability Theory and Related Fields, vol.410, issue.6825, pp.431-461, 2014. ,
DOI : 10.1007/s00440-014-0576-6
Similar but Different: Dynamic Social Network Analysis Highlights Fundamental Differences between the Fission-Fusion Societies of Two Equid Species, the Onager and Grevy???s Zebra, PLOS ONE, vol.17, issue.10, p.138645, 2015. ,
DOI : 10.1371/journal.pone.0138645.s001
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
Acrossyear social stability shapes network structure in wintering migrant sparrows, Ecol. Lett, issue.8, pp.17-998, 2014. ,
Statistical Models for Social Networks, Annual Review of Sociology, vol.37, issue.1, pp.129-151, 2011. ,
DOI : 10.1146/annurev.soc.012809.102709
Identifiability of Mixtures of Product Measures, The Annals of Mathematical Statistics, vol.38, issue.4, pp.1300-1302, 1967. ,
DOI : 10.1214/aoms/1177698805