# Hyperbolic Graph Embedders for Link Prediction and Topology Reconstruction

arXiv: https://arxiv.org/abs/2608.07029 · HTML: https://mlsanity.com/paper/2608.07029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 16 of 16)

- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry](https://mlsanity.com/paper/1806.03417/) (2018)
- [GraphWorld: Fake Graphs Bring Real Insights for GNNs](https://mlsanity.com/paper/2203.00112/) (2022)
- [Network Geometry](https://mlsanity.com/paper/2001.03241/) (2020)
- [Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs](https://mlsanity.com/paper/2206.09144/) (2022)
- [Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space](https://mlsanity.com/paper/1602.06522/) (2016)
- [Systematic comparison of graph embedding methods in practical tasks](https://mlsanity.com/paper/2106.10198/) (2021)
- [Mercator: uncovering faithful hyperbolic embeddings of complex networks](https://mlsanity.com/paper/1904.10814/) (2019)
- [Network Geometry Inference using Common Neighbors](https://mlsanity.com/paper/1502.05578/) (2015)
- [Link prediction with hyperbolic geometry](https://mlsanity.com/paper/1903.08810/) (2019)
- [A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities](https://mlsanity.com/paper/1707.07325/) (2017)
- [Efficiently Generating Geometric Inhomogeneous and Hyperbolic Random Graphs](https://mlsanity.com/paper/1905.06706/) (2019)
- [Discrete Hyperbolic Random Graph Model](https://mlsanity.com/paper/2109.11772/) (2021)
- [Modelling brain connectomes networks: Solv is a worthy competitor to hyperbolic geometry!](https://mlsanity.com/paper/2407.16077/) (2024)

## Abstract (arXiv)

Hyperbolic embeddings provide compact geometric representations of complex networks in hyperbolic spaces, but systematic comparisons of methods developed in machine learning, network science, and algorithmics remain rare. We benchmark 13 unsupervised hyperbolic graph embedders under a unified protocol for link prediction and topology reconstruction on synthetic and empirical networks. The protocol captures both missing-link recovery and the preservation of local and global network structure. Maximum-likelihood and representation-learning-based approaches, including hybrid variants, achieve the strongest overall performance, although no method dominates across all tasks and structural regimes. Performance is more strongly associated with embedding paradigm than with disciplinary origin. We identify the network regimes in which different paradigms succeed or fail and provide practical guidance for method selection in downstream applications.