Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Probabilistic Backbone of Data-Driven Complex Networks: An example
  in Climate   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.

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The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
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The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys