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VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates

arXiv 2402.18805 ↗First posted 29 Feb 2024cs.SI stat.MLcited by 0

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Abstract (arXiv)

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.

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References · 20

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8 more
  1. 2021An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guaranteescited by 10
  2. 2014Multilayer stochastic block models reveal the multilayer structure of complex networkscited by 7
  3. 2021Optimal Clustering in Anisotropic Gaussian Mixture Modelscited by 7
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  7. 2023A pseudo-likelihood approach to community detection in weighted networkscited by 2
  8. 2023The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edgescited by 1

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