From Node Embedding To Community Embedding : A Hyperbolic Approach

Detecting communities on graphs has received significant interest in recent literature. Current state-of-the-art community embedding approach called $\mathit{\text{ComE}}$ 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 $\mathit{\text{ComE}}$. 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 $\mathit{\text{ComE}}$ and recent hyperbolic-based classification approaches.