2024-04-18-braun24a.md

title	software	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates	https://github.com/glmbraun/VECSBM	Social networks are often associated with rich side information, such as texts and images. While numerous methods have been developed to identify communities from pairwise interactions, they usually ignore such side information. In this work, we study an extension of the Stochastic Block Model (SBM), a widely used statistical framework for community detection, that integrates vectorial edges covariates: the Vectorial Edges Covariates Stochastic Block Model (VEC-SBM). We propose a novel algorithm based on iterative refinement techniques and show that it optimally recovers the latent communities under the VEC-SBM. Furthermore, we rigorously assess the added value of leveraging edge’s side information in the community detection process. We complement our theoretical results with numerical experiments on synthetic and semi-synthetic data.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	braun24a	0	{VEC-SBM}: Optimal Community Detection with Vectorial Edges Covariates	532	540	532-540	532	false	Braun, Guillaume and Sugiyama, Masashi	given family Guillaume Braun given family Masashi Sugiyama	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/braun24a/braun24a.pdf