# Uncovering the hidden core-periphery structure in hyperbolic networks

arXiv: https://arxiv.org/abs/2406.19953 · HTML: https://mlsanity.com/paper/2406.19953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Identification of core-periphery structure in networks](https://mlsanity.com/paper/1409.4813/) (2014)
- [Mercator: uncovering faithful hyperbolic embeddings of complex networks](https://mlsanity.com/paper/1904.10814/) (2019)
- [Emergence of Soft Communities from Geometric Preferential Attachment](https://mlsanity.com/paper/1501.06835/) (2015)
- [A Clarified Typology of Core-Periphery Structure in Networks](https://mlsanity.com/paper/2005.10191/) (2020)
- [A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities](https://mlsanity.com/paper/1707.07325/) (2017)
- [Finding multiple core-periphery pairs in networks](https://mlsanity.com/paper/1702.06903/) (2017)
- [A Unified Method of Detecting Core-Periphery Structure and Community Structure in Networks](https://mlsanity.com/paper/1612.01704/) (2016)
- [Detecting Core-Periphery Structures by Surprise](https://mlsanity.com/paper/1810.04717/) (2018)

## Abstract (arXiv)

The hyperbolic network models exhibit very fundamental and essential features, like small-worldness, scale-freeness, high-clustering coefficient, and community structure. In this paper, we comprehensively explore the presence of an important feature, the core-periphery structure, in the hyperbolic network models, which is often exhibited by real-world networks. We focused on well-known hyperbolic models such as popularity-similarity optimization model (PSO) and S1/H2 models and studied core-periphery structures using a well-established method that is based on standard random walk Markov chain model. The observed core-periphery centralization values indicate that the core-periphery structure can be very pronounced under certain conditions. We also validate our findings by statistically testing for the significance of the observed core-periphery structure in the network geometry. This study extends network science and reveals core-periphery insights applicable to various domains, enhancing network performance and resiliency in transportation and information systems.