Mixed membership stochastic blockmodels, The Journal of Machine Learning Research, vol.9, pp.1981-2014, 2008. ,
Posterior bayes factors (disc: p128-142), Journal of the Royal Statistical Society, Series B: Methodological, vol.53, pp.111-128, 1991. ,
Assessing a mixture model for clustering with the integrated completed likelihood. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.7, pp.22719-725, 2000. ,
Dynamic topic models, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.113-120, 2006. ,
DOI : 10.1145/1143844.1143859
Latent dirichlet allocation, J. Mach. Learn. Res, vol.3, pp.993-1022, 2003. ,
Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, vol.2008, issue.10, 2008. ,
DOI : 10.1088/1742-5468/2008/10/P10008
URL : https://hal.archives-ouvertes.fr/hal-01146070
The stochastic topic block model for the clustering of vertices in networks with textual edges, Statistics and Computing, vol.31, issue.9, 2016. ,
DOI : 10.1145/1135777.1135807
URL : https://hal.archives-ouvertes.fr/hal-01299161
A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, 1991. ,
DOI : 10.1016/0167-9473(92)90042-E
URL : https://hal.archives-ouvertes.fr/inria-00075196
Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood, Statistical Modelling: An International Journal, vol.4, issue.6, pp.564-589, 2015. ,
DOI : 10.1214/10-AOAS359
Modelling time evolving interactions in networks through a non stationary extension of stochastic block models, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM '15, pp.1590-1591, 2015. ,
DOI : 10.1016/j.jtbi.2010.11.033
URL : https://hal.archives-ouvertes.fr/hal-01263540
Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL, Social Network Analysis and Mining, vol.82, issue.2, pp.1-14, 2016. ,
DOI : 10.1007/s10994-010-5214-7
URL : https://hal.archives-ouvertes.fr/hal-01468548
Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks, Neurocomputing, vol.192, pp.81-91, 2016. ,
DOI : 10.1016/j.neucom.2016.02.031
URL : https://hal.archives-ouvertes.fr/hal-01312596
Multiple change points detection and clustering in dynamic networks, Statistics and Computing, vol.32, issue.2, 2017. ,
DOI : 10.1007/s00180-016-0655-5
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
Locally adaptive dynamic networks, The Annals of Applied Statistics, vol.10, issue.4, pp.2203-2232, 2016. ,
DOI : 10.1214/16-AOAS971
Interlocking directorates in Irish companies using a latent space model for bipartite networks, Proceedings of the National Academy of Sciences, pp.1136629-6634, 2016. ,
DOI : 10.1111/1467-9868.00353
topicmodels: An R package for fitting topic models, Journal of Statistical Software, issue.13, pp.401-431, 2011. ,
A Triclustering Approach for Time Evolving Graphs, 2012 IEEE 12th International Conference on Data Mining Workshops, pp.115-122, 2012. ,
DOI : 10.1109/ICDMW.2012.61
Discovering patterns in time-varying graphs: a triclustering approach Advances in Data Analysis and Classification, pp.1-28, 2015. ,
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
Discrete temporal models of social networks, Electronic Journal of Statistics, vol.4, issue.0, pp.585-605, 2010. ,
DOI : 10.1214/09-EJS548
Latent space approaches to social network analysis, Journal of the American Statistical Association, issue.460, pp.971090-1098, 2002. ,
DOI : 10.21236/ada458734
URL : http://www.csss.washington.edu/Papers/wp17.ps
A separable model for dynamic networks, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.31, issue.1, 2014. ,
DOI : 10.1016/j.socnet.2009.02.004
Variational Bayesian inference and complexity control for stochastic block models, Statistical Modelling: An International Journal, vol.41, issue.1, pp.93-115, 2012. ,
DOI : 10.1016/j.patcog.2008.06.019
URL : https://hal.archives-ouvertes.fr/hal-00624536
Topic-link LDA, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.665-672, 2009. ,
DOI : 10.1145/1553374.1553460
Statistical clustering of temporal networks through a dynamic stochastic block model, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.4, issue.4, 2016. ,
DOI : 10.1214/10-AOAS359
URL : https://hal.archives-ouvertes.fr/hal-01167837
Estimation and clustering in a semiparametric Poisson process stochastic block model for longitudinal networks, 2015. ,
The author-recipient-topic model for topic and role discovery in social networks, Workshop on Link Analysis, Counterterrorism and Security, 2005. ,
Finding and evaluating community structure in networks, Physical Review E, vol.65, issue.2, p.26113, 2004. ,
DOI : 10.1103/PhysRevE.68.065103
Bayesian non parametric inference of discrete valued networks, 21-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.291-296, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00825966
Estimation and Prediction for Stochastic Blockstructures, Journal of the American Statistical Association, vol.96, issue.455, pp.961077-1087, 2001. ,
DOI : 10.1198/016214501753208735
Social topic models for community extraction, 2008. ,
Detecting change points in the large-scale structure of evolving networks, 2014. ,
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
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
The author-topic model for authors and documents, Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, UAI '04, pp.487-494, 2004. ,
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
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://www.sigkdd.org/explorations/issues/7-2-2005-12/4-Sarkar.pdf
Latent Space Models for Dynamic Networks, Journal of the American Statistical Association, vol.110, issue.512, pp.1646-1657, 2015. ,
DOI : 10.1038/30918
Latent space models for dynamic networks with weighted edges, Social Networks, vol.44, pp.105-116, 2016. ,
DOI : 10.1016/j.socnet.2015.07.005
Probabilistic authortopic models for information discovery, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.306-315, 2004. ,
A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007. ,
DOI : 10.1017/CBO9780511810633
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
On the Convergence Properties of the EM Algorithm, The Annals of Statistics, vol.11, issue.1, pp.95-103, 1983. ,
DOI : 10.1214/aos/1176346060
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
Detecting communities and their evolutions in dynamic social networks???a??Bayesian approach, Machine Learning, vol.2, issue.1, pp.157-189, 2011. ,
DOI : 10.1017/CBO9780511815478
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
The dynamic random subgraph model for the clustering of evolving networks, Computational Statistics, vol.31, issue.9, pp.1-33, 2016. ,
DOI : 10.1016/j.patrec.2010.01.026
URL : https://hal.archives-ouvertes.fr/hal-01122393