  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.
