ArticleProceedings. Mathematical, physical, and engineering sciences2020
Deriving pairwise transfer entropy from network structure and motifs.
Article in Proceedings. Mathematical, physical, and engineering sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- On the potential of transfer entropy in turbulent dynamical systems.Scientific reports · 2023Article
- Analytic relationship of relative synchronizability to network structure and motifs.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Normalizing the brain connectome for communication through synchronization.Network neuroscience (Cambridge, Mass.) · 2022Article
- Early lock-in of structured and specialised information flows during neural development.eLife · 2022Article
- A perturbative approach to study information communication in brain networks.Network neuroscience (Cambridge, Mass.) · 2022Article
- Inferring network properties from time series using transfer entropy and mutual information: Validation of multivariate versus bivariate approaches.Network neuroscience (Cambridge, Mass.) · 2021Article
- Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age.Network neuroscience (Cambridge, Mass.) · 2020Article
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Abstract
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 topology. It is shown analytically that the dependence on the directed link weight is only a first approximation, valid for weak coupling. More generally, the TE increases with the in-degree of the source and decreases with the in-degree of the target, indicating an asymmetry of information transfer between hubs and low-degree nodes. In addition, the TE is directly proportional to weighted motif counts involving common parents or multiple walks from the source to the target, which are more abundant in networks with a high clustering coefficient than in random networks. Our findings also apply to Granger causality, which is equivalent to TE for Gaussian variables. Moreover, similar empirical results on random Boolean networks suggest that the dependence of the TE on the in-degree extends to nonlinear dynamics.
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