Bayesian Methods for Graph Clustering. - Pierre Latouche; Etienne Birmelé; Christophe Ambroise (GfKl, 2008)

Bayesian non parametric inference of discrete valued networks. - Laetitia Nouedoui; Pierre Latouche (ESANN, 2013)

Activity Date Estimation in Timestamped Interaction Networks. - Fabrice Rossi; Pierre Latouche (ESANN, 2013)

Overlapping Clustering Methods for Networks. - Pierre Latouche; Etienne Birmelé; Christophe Ambroise (Handbook of Mixed Membership Models and Their Applications, 2014)

Modelling time evolving interactions in networks through a non stationary extension of stochastic block models. - Marco Corneli; Pierre Latouche; Fabrice Rossi (ASONAM, 2015)

Is the corporate elite disintegrating?: Interlock boards and the Mizruchi hypothesis. - Kevin Mentzer; François-Xavier Dudouet; Dominique Haughton; Pierre Latouche; Fabrice Rossi (ASONAM, 2015)

Exact ICL maximization in a non-stationary time extension of latent block model for dynamic networks. - Marco Corneli; Pierre Latouche; Fabrice Rossi (ESANN, 2015)

Graphs in machine learning. An introduction. - Pierre Latouche; Fabrice Rossi (ESANN, 2015)

A State-Space Model for the Dynamic Random Subgraph Model. - Rawya Zreik; Pierre Latouche; Charles Bouveyron (ESANN, 2015)

Globally Sparse Probabilistic PCA. - Pierre-Alexandre Mattei; Charles Bouveyron; Pierre Latouche (AISTATS, 2016)

Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks. - Marco Corneli; Pierre Latouche; Fabrice Rossi (Neurocomputing, 2016)

Combining a relaxed EM algorithm with Occam's razor for Bayesian variable selection in high-dimensional regression. - Pierre Latouche; Pierre-Alexandre Mattei; Charles Bouveyron; Julien Chiquet (J. Multivar. Anal., 2016)

Variational Bayes model averaging for graphon functions and motif frequencies inference in W-graph models. - Pierre Latouche; Stéphane Robin (Stat. Comput., 2016)

Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL. - Marco Corneli; Pierre Latouche; Fabrice Rossi (Soc. Netw. Anal. Min., 2016)

The dynamic random subgraph model for the clustering of evolving networks. - Rawya Zreik; Pierre Latouche; Charles Bouveyron (Comput. Stat., 2017)

Inferring structure in bipartite networks using the latent blockmodel and exact ICL. - Jason Wyse; Nial Friel; Pierre Latouche (Netw. Sci., 2017)

Choosing the number of groups in a latent stochastic blockmodel for dynamic networks. - Riccardo Rastelli; Pierre Latouche; Nial Friel (Netw. Sci., 2018)

The stochastic topic block model for the clustering of vertices in networks with textual edges. - Charles Bouveyron; Pierre Latouche; Rawya Zreik (Stat. Comput., 2018)

Multiple change points detection and clustering in dynamic networks. - Marco Corneli; Pierre Latouche; Fabrice Rossi (Stat. Comput., 2018)

The latent topic block model for the co-clustering of textual interaction data. - Laurent R. Bergé; Charles Bouveyron; Marco Corneli; Pierre Latouche (Comput. Stat. Data Anal., 2019)

The dynamic stochastic topic block model for dynamic networks with textual edges. - Marco Corneli; Charles Bouveyron; Pierre Latouche; Fabrice Rossi (Stat. Comput., 2019)

MAGMA: Inference and Prediction with Multi-Task Gaussian Processes. - Arthur Leroy; Pierre Latouche; Benjamin Guedj; Servane Gey (CoRR, 2020)

Hierarchical clustering with discrete latent variable models and the integrated classification likelihood. - Etienne Côme; Nicolas Jouvin; Pierre Latouche; Charles Bouveyron (Adv. Data Anal. Classif., 2021)

Greedy clustering of count data through a mixture of multinomial PCA. - Nicolas Jouvin; Pierre Latouche; Charles Bouveyron; Guillaume Bataillon; Alain Livartowski (Comput. Stat., 2021)

DeepLTRS: A deep latent recommender system based on user ratings and reviews. - Dingge Liang; Marco Corneli; Charles Bouveyron; Pierre Latouche (Pattern Recognit. Lett., 2021)

A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering. - Nicolas Jouvin; Charles Bouveyron; Pierre Latouche (Stat. Comput., 2021)

Deep latent position model for node clustering in graphs. - Dingge Liang; Marco Corneli; Charles Bouveyron; Pierre Latouche (ESANN, 2022)

MAGMA: inference and prediction using multi-task Gaussian processes with common mean. - Arthur Leroy; Pierre Latouche; Benjamin Guedj; Servane Gey (Mach. Learn., 2022)

The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges. - Rémi Boutin; Pierre Latouche; Charles Bouveyron (CoRR, 2023)

Cluster-Specific Predictions with Multi-Task Gaussian Processes. - Arthur Leroy; Pierre Latouche; Benjamin Guedj; Servane Gey (J. Mach. Learn. Res., 2023)

Embedded topics in the stochastic block model. - Rémi Boutin; Charles Bouveyron; Pierre Latouche (Stat. Comput., 2023)

The graph embedded topic model. - Dingge Liang; Marco Corneli; Charles Bouveyron; Pierre Latouche (Neurocomputing, 2023)

The multiplex deep latent position model for the clustering of nodes in multiview networks. - Dingge Liang; Marco Corneli; Charles Bouveyron; Pierre Latouche; Junping Yin (Neurocomputing, 2025)

Clustering by deep latent position model with graph convolutional network. - Dingge Liang; Marco Corneli; Charles Bouveyron; Pierre Latouche (Adv. Data Anal. Classif., 2025)

A new machine learning framework for occupational accidents forecasting with safety inspections integration. - Aho Yapi; Pierre Latouche; Arnaud Guillin; Yan Bailly (CoRR, 2025)

The Deep Latent Position Block Model for Block Clustering and Latent Representation of Nodes in Networks. - Rémi Boutin; Pierre Latouche; Charles Bouveyron (Stat. Comput., 2025)