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Deriving pairwise transfer entropy from network structure and motifs.

Leonardo Novelli1, Fatihcan M Atay2,3, Jürgen Jost3,4

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Proceedings. Mathematical, Physical, and Engineering Sciences
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PubMed
Summary
This summary is machine-generated.

Transfer entropy (TE) quantifies directed dependencies in networks. Network structure, not just link weight, influences information transfer, impacting hubs and low-degree nodes differently.

Keywords:
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Area of Science:

  • Complex Systems
  • Neuroimaging
  • Network Science

Background:

  • Transfer entropy (TE) quantifies directed statistical dependencies in complex systems and neuroimaging.
  • Pairwise TE depends not only on local link weights but also on the broader network structure.

Purpose of the Study:

  • To analytically derive transfer entropy for each link based on network topology.
  • To investigate how network structure influences information transfer quantification.

Main Methods:

  • Utilized a discrete-time linearly coupled Gaussian model.
  • Derived TE analytically from network topology.
  • Analyzed dependence on in-degree and weighted motif counts.

Main Results:

  • TE dependence on directed link weight is an approximation for weak coupling.
  • TE increases with source in-degree and decreases with target in-degree, showing asymmetry.
  • TE correlates with weighted motif counts and network clustering coefficient.

Conclusions:

  • Network topology significantly shapes information transfer beyond local link weights.
  • In-degree asymmetry highlights differential information flow between network hubs and peripheral nodes.
  • Findings extend to Granger causality and nonlinear dynamics in random Boolean networks.