From Node Embedding To Community Embedding : A Hyperbolic Approach

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.

This version replaces the previous one. The package generating the experimental results will be made public in the near future

References in corpus (7)

• Distributed Representations of Words and Phrases and their Compositionality
• Hyperbolic Geometry of Complex Networks
• graph2vec: Learning Distributed Representations of Graphs
• Sustaining the Internet with Hyperbolic Mapping
• From Node Embedding To Community Embedding
• Poincaré Wasserstein Autoencoder
• Metric Learning on Manifolds

Cited by in corpus (1)

• Riemannian Gaussian distributions, random matrix ensembles and diffusion kernels