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}}
2025
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
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
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}}
2024
Conditional Denoising Diffusion Probabilistic Models for the Clustering of Images
Seydina Ousmane Niang, Charles Bouveyron, Marco Corneli, and 1 more author
In 55èmes Journées de Statistiques de la SFdS (JDS 2024), May 2024
We propose a conditional extension of Denoising Diffusion Probabilistic Models (DDPMs) for clustering image data. The model assumes images are distributed into Q clusters, and a neural network is trained to predict the noise to remove during generation based on cluster membership. Inference is performed using a variational EM-type algorithm combining the advantages of diffusion models with a principled clustering approach.
@inproceedings{niang2024diffusion,title={Conditional Denoising Diffusion Probabilistic Models for the Clustering of Images},author={Niang, Seydina Ousmane and Bouveyron, Charles and Corneli, Marco and Latouche, Pierre},booktitle={55\`emes Journ{\'e}es de Statistiques de la {SFdS} ({JDS} 2024)},year={2024},month=may,address={Bordeaux, France},pages={1367--1374},keywords={Diffusion probabilistic models, Image clustering, Variational inference, EM algorithm},}
Deep Latent Position Block Model for the Clustering of Nodes in Multi-Graphs
Seydina Ousmane Niang, Charles Bouveyron, Marco Corneli, and 2 more authors
Poster presented at a conference on the Deep Latent Position Block Model for multidimensional networks (Multi-Graphs), combining block modelling and latent representation via a variational autoencoder with graph convolutional networks.
@misc{niang2024poster,title={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},year={2024},keywords={Network clustering, Latent position model, Graph convolutional network, Poster},note={Poster de conf{\'e}rence}}