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Article Dans Une Revue Data Mining and Knowledge Discovery Année : 2023

A hyperbolic approach for learning communities on graphs

Thomas Gerald
Hadi Zaatiti
Hatem Hajri
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Nicolas Baskiotis

Résumé

Detecting communities on graphs has received significant interest in recent literature. Current state-of-the-art approaches tackle this problem by coupling Euclidean graph embedding with community detection. Considering the success of hyperbolic representations of graph-structured data in the last years, an ongoing challenge is to set up a hyperbolic approach to the community detection problem. The present paper meets this challenge by introducing a Riemannian geometry based framework for learning communities on graphs. The proposed methodology combines graph embedding on hyperbolic spaces with Riemannian K-means or Riemannian mixture models to perform community detection. The usefulness of this framework is illustrated through several experiments on generated community graphs and real-world social networks as well as comparisons with the most powerful baselines. The code implementing hyperbolic community embedding is available online https://www.github.com/tgeral68/HyperbolicGraphAndGMM.
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hal-04022426 , version 1 (28-02-2024)

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Thomas Gerald, Hadi Zaatiti, Hatem Hajri, Nicolas Baskiotis, Olivier Schwander. A hyperbolic approach for learning communities on graphs. Data Mining and Knowledge Discovery, 2023, 37, pp.1090-1124. ⟨10.1007/s10618-022-00902-8⟩. ⟨hal-04022426⟩
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