2022-06-28-braun22a.md

title	booktitle	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
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees	Proceedings of the 39th International Conference on Machine Learning	Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network clustering methods during the past decade, the added value of side information and the methods used to incorporate it optimally in clustering algorithms are relatively less understood. We propose a new iterative algorithm to cluster networks with side information for nodes (in the form of covariates) and show that our algorithm is optimal under the Contextual Symmetric Stochastic Block Model. Our algorithm can be applied to general Contextual Stochastic Block Models and avoids hyperparameter tuning in contrast to previously proposed methods. We confirm our theoretical results on synthetic data experiments where our algorithm significantly outperforms other methods, and show that it can also be applied to signed graphs. Finally we demonstrate the practical interest of our method on real data.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	braun22a	0	An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees	2257	2291	2257-2291	2257	false	Braun, Guillaume and Tyagi, Hemant and Biernacki, Christophe	given family Guillaume Braun given family Hemant Tyagi given family Christophe Biernacki	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/braun22a/braun22a.pdf