Seydina Ousmane NIANG

Laboratoire J.A. Dieudonné

Université Nice Côte d'Azur

Nice, France

I am a third-year PhD student at the Laboratoire J.A. Dieudonné, Université Nice Côte d’Azur, under the supervision of Prof. Charles Bouveyron, Dr. Marco Corneli, and Prof. Pierre Latouche.

My PhD research focuses on deep generative models for the joint analysis of networks and continuous data. More broadly, my research interests lie at the intersection of probabilistic modelling, statistical learning, and machine learning, with a particular emphasis on:

Graph-structured data and network analysis
Deep generative models (variational autoencoders, latent variable models)
Probabilistic inference and variational methods
Applied statistics and high-dimensional data

Feel free to reach out by email for any question related to my research.

news

Nov 30, 2025	Invited talk at the 3IA Côte d’Azur Postdoctoral Seminar, Nice, France — The Deep Latent Position Block Model for the Clustering of Nodes in Multi-Graphs. [Slides available upon request.]
Oct 31, 2025	Poster presentation at Sophia Summit 2025, Sophia Antipolis, France — The Deep Zero-Inflated Latent Position Block Model for the Clustering of Nodes in Graphs. [Poster available upon request.]
Jul 01, 2025	Poster presentation at the Machine Learning Summer School, Senegal — Importance Weighted Directed Graph Variational Auto-Encoder for Block Modelling of Complex Networks. [Poster available upon request.]
May 31, 2025	Oral presentation at the 56th Journées de Statistiques de la SFdS (JDS 2025), Marseille, France — The Deep Zero-Inflated Latent Position Block Model for the Clustering of Nodes in Graphs. [Slides available upon request.]
Oct 31, 2024	Poster presentation at Sophia Summit 2024, Sophia Antipolis, France — Conditional Denoising Diffusion Probabilistic Models for the Clustering of Images. [Poster available upon request.]

selected publications

The Deep Latent Position Block Model for the Clustering of Nodes in Multi-graphs

Seydina Ousmane Niang, Charles Bouveyron, Marco Corneli, and 2 more authors

Journal of Classification, Mar 2026

Published online: 12 March 2026

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Network data capture relationships among actors across multiple contexts, often forming clusters of individuals. These relationships frequently involve multiple types of interactions, requiring multidimensional networks, or multi-graphs, to fully capture their complexity. Latent position models (LPMs) embed nodes based on connection probabilities but cannot uncover heterogeneous clustering structures such as disassortative patterns. Stochastic block models (SBMs) excel at clustering but lack interpretable latent representations. To address these limitations, we introduce Deep-LPBMM, extending the deep latent position block model to multidimensional networks using a deep variational autoencoder with graph convolutional networks across multiple interaction types, enabling both effective clustering and enhanced visualization.
@article{niang2026deeplpbmm, title = {The Deep Latent Position Block Model for the Clustering of Nodes in Multi-graphs}, author = {Niang, Seydina Ousmane and Bouveyron, Charles and Corneli, Marco and Latouche, Pierre and Boutin, R{\'e}mi}, journal = {Journal of Classification}, year = {2026}, month = mar, doi = {10.1007/s00357-026-09541-w}, keywords = {Stochastic block model, Latent position model, Clustering, Multidimensional network analysis, Multi-graphs, Variational autoencoder, Graph convolutional network}, note = {Published online: 12 March 2026} }
The Deep Zero-Inflated Latent Position Block Model for the Clustering of Nodes in Graphs

Seydina Ousmane Niang, Charles Bouveyron, Marco Corneli, and 1 more author

In Communication dans un Congrès, 2025

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We introduce a deep generative model for the clustering of nodes in sparse networks, combining zero-inflated modelling with a latent position block structure. The model provides simultaneous node clustering and latent space visualisation while accounting for the excess of zeros in network data.
@inproceedings{niang2025deepzilpbm, title = {The Deep Zero-Inflated Latent Position Block Model for the Clustering of Nodes in Graphs}, author = {Niang, Seydina Ousmane and Bouveyron, Charles and Corneli, Marco and Latouche, Pierre}, booktitle = {Communication dans un Congr{\`e}s}, year = {2025}, keywords = {Zero-inflated model, Network clustering, Deep generative model, Latent position model}, }
Importance Weighted Directed Graph Variational Auto-Encoder for Block Modelling of Complex Networks

Seydina Ousmane Niang, Charles Bouveyron, Marco Corneli, and 1 more author

2025

Preprint

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We introduce the first importance weighted graph variational autoencoder for weighted and directed networks: the Deep Zero-Inflated Latent Position Block Model (Deep-ZLPBM). By optimising the importance-weighted ELBO (iw-ELBO) with multiple samples from the variational distribution, we obtain tighter lower bounds, more accurate posterior approximations, and better latent embeddings for clustering and visualisation of complex networks.
@unpublished{niang2025iwgvae, title = {Importance Weighted Directed Graph Variational Auto-Encoder for Block Modelling of Complex Networks}, author = {Niang, Seydina Ousmane and Bouveyron, Charles and Corneli, Marco and Latouche, Pierre}, year = {2025}, keywords = {Importance weighting, Variational autoencoder, Graph neural network, Network clustering, Zero-inflated model}, note = {Preprint} }