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

Abstract

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.

References in corpus (6)

• Fast unfolding of communities in large networks
• Finding and evaluating community structure in networks
• Finding statistically significant communities in networks
• The map equation
• Complex networks in climate dynamics - Comparing linear and nonlinear network construction methods
• The backbone of the climate network

Cited by in corpus (1)

• Learning complex dependency structure of gene regulatory networks from high dimensional micro-array data with Gaussian Bayesian networks