Hyperbolic Graph Embeddings: a Survey and an Evaluation on Anomaly Detection

Souhail Abdelmouaiz Sadat, Mohamed Yacine Touahria Miliani, Khadidja Hab El Hames, Hamida Seba, Mohammed Haddad
12/21/2025
cs.LGcs.AI

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Abstract

This survey reviews hyperbolic graph embedding models, and evaluate them on anomaly detection, highlighting their advantages over Euclidean methods in capturing complex structures. Evaluating models like \textit{HGCAE}, \textit{\(\mathcal{P}\)-VAE}, and \textit{HGCN} demonstrates high performance, with \textit{\(\mathcal{P}\)-VAE} achieving an F1-score of 94\% on the \textit{Elliptic} dataset and \textit{HGCAE} scoring 80\% on \textit{Cora}. In contrast, Euclidean methods like \textit{DOMINANT} and \textit{GraphSage} struggle with complex data. The study emphasizes the potential of hyperbolic spaces for improving anomaly detection, and provides an open-source library to foster further research in this field.

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Cite this paper

@article{sadat2025hyperbolic,
  title  = {Hyperbolic Graph Embeddings: a Survey and an Evaluation on Anomaly Detection},
  author = {Souhail Abdelmouaiz Sadat and Mohamed Yacine Touahria Miliani and Khadidja Hab El Hames and Hamida Seba and Mohammed Haddad},
  year   = {2025},
  eprint = {2512.18826},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.18826v1}
}

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