Rémi Boutin

I hold a postdoc position at the LPSM laboratory, Sorbonne Université, under the supervision of Catherine Matias. My work focuses on node clustering in sparse hypergraphs.

I obained my PhD in 2023 at the MAP 5 laboratory, Université Paris Cité, for my thesis on Deep graphical models and inference strategies for the analysis of networks comprising textual edges under the supervision of Pierre Latouche and Charles Bouveyron.

My areas of research are related to statistical network analysis, network clustering, topic modelling as well as deep probabilistic modelling. Feel free to send me an email for more information concerning my work.

news

Dec 14, 2023	PhD defence in front of the jury composed of Etienne Côme (examinateur), Sophie Donnet (rapporteuse), Brendan Murphy (rapporteur), Tabea Rebafka (examinatrice), Cinzia Viroli (examinatrice). The manuscript is available here and the slides of my PhD defence here.
Jul 4, 2023	Presentation at the Journées de la Statistique 2023 on the Deep Latent Position Topic Model. The slides are available here
Apr 6, 2023	Poster session at Statlearn 2023 on the Deep Latent Position Topic Model, the poster is accessible here
Nov 15, 2022	Presentation during the seminar of Probability, Analysis and Statistic at the Laboratoire de Mathématiques Blaise Pascal, Université Clermont Auvergne.
Nov 9, 2022	Introduction to probabilistic modelling and variational inference during the seminar of PhD students, at the Laboratoire de Mathématiques Blaise Pascal, Université Clermont Auvergne.
Oct 14, 2022	Presentation during the seminar of PhD students, at the MAP5, Université Paris Cité.
Jun 13, 2022	Presentation during the Journées de la Statistique 2022, in Lyon. The slides are available here
Apr 6, 2022	Poster session at Statlearn 2022, in Cargèse.
Nov 6, 2021	Poster session during the SophIA summit, the poster is available here

selected publications

The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges

Rémi Boutin, Pierre Latouche, and Charles Bouveyron

arXiv preprint arXiv:2304.08242, 2023

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Numerical interactions leading to users sharing textual content published by others are naturally represented by a network where the individuals are associated with the nodes and the exchanged texts with the edges. To understand those heterogeneous and complex data structures, clustering nodes into homogeneous groups as well as rendering a comprehensible visualisation of the data is mandatory. To address both issues, we introduce Deep-LPTM, a model-based clustering strategy relying on a variational graph auto-encoder approach as well as a probabilistic model to characterise the topics of discussion. Deep-LPTM allows to build a joint representation of the nodes and of the edges in two embeddings spaces. The parameters are inferred using a variational inference algorithm. We also introduce IC2L, a model selection criterion specifically designed to choose models with relevant clustering and visualisation properties. An extensive benchmark study on synthetic data is provided. In particular, we find that Deep-LPTM better recovers the partitions of the nodes than the state-of-the art ETSBM and STBM. Eventually, the emails of the Enron company are analysed and visualisations of the results are presented, with meaningful highlights of the graph structure.
@article{boutin2023deep, title = {The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges}, author = {Boutin, R{\'e}mi and Latouche, Pierre and Bouveyron, Charles}, journal = {arXiv preprint arXiv:2304.08242}, year = {2023}, }
Embedded Topics in the Stochastic Block Model

Rémi Boutin, Charles Bouveyron, and Pierre Latouche

Statistics and Computing, 2023

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Communication networks such as emails or social networks are now ubiquitous and their analysis has become a strategic field. In many applications, the goal is to automatically extract relevant information by looking at the nodes and their connections. Unfortunately, most of the existing methods focus on analysing the presence or absence of edges and textual data is often discarded. However, all communication networks actually come with textual data on the edges. In order to take into account this specificity, we consider in this paper networks for which two nodes are linked if and only if they share textual data. We introduce a deep latent variable model allowing embedded topics to be handled called ETSBM to simultaneously perform clustering on the nodes while modelling the topics used between the different clusters. ETSBM extends both the stochastic block model (SBM) and the embedded topic model (ETM) which are core models for studying networks and corpora, respectively. The inference is done using a variational-Bayes expectation-maximisation algorithm combined with a stochastic gradient descent. The methodology is evaluated on synthetic data and on a real world dataset.
@article{boutin2023embedded, title = {Embedded Topics in the Stochastic Block Model}, author = {Boutin, R{\'e}mi and Bouveyron, Charles and Latouche, Pierre}, journal = {Statistics and Computing}, volume = {33}, number = {5}, pages = {1--20}, year = {2023}, publisher = {Springer}, }