The Probabilistic Backbone of Data-Driven Complex Networks: An example in Climate
This addresses the issue of network redundancy for researchers in complex systems and climate science, but it is incremental as it applies an existing method to a new domain.
The paper tackles the problem of redundant information in Correlation Networks by advocating for Bayesian Networks as a probabilistic backbone, resulting in a sparse topology that extracts generalizable physical features, demonstrated on a global climate dataset.
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.