Deriving pairwise transfer entropy from network structure and motifs

@article{Novelli2020DerivingPT,
  title={Deriving pairwise transfer entropy from network structure and motifs},
  author={Leonardo Novelli and F. Atay and J. Jost and J. Lizier},
  journal={Proceedings of the Royal Society A},
  year={2020},
  volume={476}
}
  • Leonardo Novelli, F. Atay, +1 author J. Lizier
  • Published 2020
  • Computer Science, Medicine, Mathematics, Physics, Biology
  • Proceedings of the Royal Society A
  • Transfer entropy (TE) is an established method for quantifying directed statistical dependencies in neuroimaging and complex systems datasets. The pairwise (or bivariate) TE from a source to a target node in a network does not depend solely on the local source-target link weight, but on the wider network structure that the link is embedded in. This relationship is studied using a discrete-time linearly coupled Gaussian model, which allows us to derive the TE for each link from the network… CONTINUE READING
    3 Citations

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