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arXiv · 1907.01662

From Node Embedding To Community Embedding : A Hyperbolic Approach

Thomas Gerald·Hadi Zaatiti·Hatem Hajri·Nicolas Baskiotis·Olivier Schwander
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Abstract

Detecting communities on graphs has received significant interest in recent literature. Current state-of-the-art community embedding approach called \textit{ComE} tackles this problem by coupling graph embedding with community detection. Considering the success of hyperbolic representations of graph-structured data in last years, an ongoing challenge is to set up a hyperbolic approach for the community detection problem. The present paper meets this challenge by introducing a Riemannian equivalent of \textit{ComE}. Our proposed approach combines hyperbolic embeddings with Riemannian K-means or Riemannian mixture models to perform community detection. We illustrate the usefulness of this framework through several experiments on real-world social networks and comparisons with \textit{ComE} and recent hyperbolic-based classification approaches.

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Thomas Gerald, Hadi Zaatiti, Hatem Hajri, Nicolas Baskiotis, Olivier Schwander. 2019-07-02. From Node Embedding To Community Embedding : A Hyperbolic Approach. https://arxiv.org/abs/1907.01662

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