ArticleScientific reports2023
On quantification and maximization of information transfer in network dynamical systems.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Detecting existence of a hidden mediator between a pair of individual time series.Scientific reports · 2025Article
- Cortical Surface Spatial Analysis Reveals Altered Brain Functional Network Topology in T2DM With Mild Cognitive Impairment.Brain and behavior · 2025Article
- Protein swarm-based cause-effect analysis: effects of microRNAs on cooperation networks linking COVID-19 infections, atherosclerosis, and Alzheimer's disease.Frontiers in cardiovascular medicine · 2025Article
- Effects of vitamin D signaling in cardiovascular disease: centrality of macrophage polarization.Frontiers in cardiovascular medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes and provides a better understanding of the contributions of these nodes individually or collectively towards the underlying network dynamics. Variations in network topologies result in varying information flows among nodes. We integrate theories from information science with control network theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework explicates the relationships between the network topology and the functional patterns, such as the information transfers in biological networks, information rerouting in sensor nodes, and influence patterns in social networks. We show that by designing or re-configuring the network topology, we can optimize the information transfer function between two chosen nodes. As a proof of concept, we apply our proposed methods in the context of brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.