  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.
