From Node Embedding To Community Embedding : A Hyperbolic Approach
This work addresses the problem of community detection for social network analysis, but it is incremental as it adapts an existing method to a hyperbolic setting.
The paper tackles community detection in graphs by introducing a hyperbolic version of the ComE method, combining hyperbolic embeddings with Riemannian clustering, and reports improved performance on real-world social networks compared to existing approaches.
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.