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Entropy Change in Reversible Processes01:10

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A split-and-transfer flow based entropic centrality.

Frédérique Oggier1, Silivanxay Phetsouvanh2, Anwitaman Datta2

  • 1Division of Mathematical Sciences, Nanyang Technological University, Singapore, Singapore.

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|April 5, 2021
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Summary
This summary is machine-generated.

This study introduces a split-and-transfer flow model for entropic centrality, allowing flow to split at nodes. This novel approach enhances network analysis by providing a more nuanced understanding of node importance in complex systems.

Keywords:
CentralityEntropyInformation flow

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Area of Science:

  • Network Science
  • Information Theory
  • Computational Social Science

Background:

  • Entropic centrality measures node importance based on flow destination uncertainty.
  • Existing models assume indivisible flow, limiting nuanced analysis.
  • This limitation overlooks the potential for flow to split at network nodes.

Purpose of the Study:

  • To propose a novel split-and-transfer flow model for entropic centrality.
  • To enable arbitrary splitting of flow across neighboring nodes.
  • To provide a computationally tractable method for analyzing this new centrality measure.

Main Methods:

  • Developed a split-and-transfer flow model for entropic centrality.
  • Mapped the new model to an equivalent transfer entropic centrality framework.
  • Applied the model to empirical network datasets.

Main Results:

  • The split-and-transfer model offers a more flexible and realistic approach to entropic centrality.
  • The mapping to transfer entropic centrality simplifies computation.
  • Case studies demonstrate the model's utility in diverse networks.

Conclusions:

  • The split-and-transfer flow model advances entropic centrality by incorporating flow divisibility.
  • This provides richer insights into node importance in complex networks.
  • The method is applicable to various network types, including transportation, financial, and digital systems.