{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-graph-structured-data-using-poincare","title":"From Node Embedding To Community Embedding : A Hyperbolic Approach","arxiv_id":"1907.01662","date":"2019-07-02","proceeding":null,"authors":["Thomas Gerald","Hadi Zaatiti","Hatem Hajri","Nicolas Baskiotis","Olivier Schwander"],"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.","url_abs":"https://arxiv.org/abs/1907.01662v2","url_pdf":"https://arxiv.org/pdf/1907.01662v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-graph-structured-data-using-poincare","repo_url":"https://github.com/drewwilimitis/hyperbolic-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"learning-graph-structured-data-using-poincare","repo_url":"https://github.com/tgeral68/HyperbolicGraphAndGMM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"graph-embedding","task_name":"Graph Embedding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}