# The Probabilistic Backbone of Data-Driven Complex Networks: An example in Climate

arXiv: https://arxiv.org/abs/1912.03758 · HTML: https://mlsanity.com/paper/1912.03758/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

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

- [The Impact of Variable Ordering on Bayesian Network Structure Learning](https://mlsanity.com/paper/2206.08952/) (2022)
- [Causal discovery using dynamically requested knowledge](https://mlsanity.com/paper/2310.11154/) (2023)
- [Learning complex dependency structure of gene regulatory networks from high dimensional micro-array data with Gaussian Bayesian networks](https://mlsanity.com/paper/2106.15365/) (2021)

## Abstract (arXiv)

Correlation Networks (CNs) inherently suffer from redundant information in their network topology. Bayesian Networks (BNs), on the other hand, include only non-redundant information (from a probabilistic perspective) resulting in a sparse topology from which generalizable physical features can be extracted. We advocate the use of BNs to construct data-driven complex networks as they can be regarded as the probabilistic backbone of the underlying complex system. Results are illustrated at the hand of a global climate dataset.