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

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