An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees

TL;DR

This paper introduces an iterative clustering algorithm for networks with node side information, achieving optimality under the Contextual Symmetric Stochastic Block Model and outperforming existing methods in synthetic and real data.

Contribution

It presents a new hyperparameter-free iterative clustering algorithm that is proven to be optimal under the Contextual Symmetric Stochastic Block Model.

Findings

Algorithm outperforms existing methods on synthetic data

Demonstrates effectiveness on signed graphs

Shows practical utility on real-world data

Abstract

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.