# Distance entropy cartography characterises centrality in complex networks

arXiv: https://arxiv.org/abs/1802.10411 · HTML: https://mlsanity.com/paper/1802.10411/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 3 of 3)

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- [Network psychometrics and cognitive network science open new ways for detecting, understanding and tackling the complexity of math anxiety: A review](https://mlsanity.com/paper/2108.13800/) (2021)
- [Cognitive modelling with multilayer networks: Insights, advancements and future challenges](https://mlsanity.com/paper/2210.00500/) (2022)

## Abstract (arXiv)

We introduce distance entropy as a measure of homogeneity in the distribution of path lengths between a given node and its neighbours in a complex network. Distance entropy defines a new centrality measure whose properties are investigated for a variety of synthetic network models. By coupling distance entropy information with closeness centrality, we introduce a network cartography which allows one to reduce the degeneracy of ranking based on closeness alone. We apply this methodology to the empirical multiplex lexical network encoding the linguistic relationships known to English speaking toddlers. We show that the distance entropy cartography better predicts how children learn words compared to closeness centrality. Our results highlight the importance of distance entropy for gaining insights from distance patterns in complex networks.