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Extracting directed information flow networks: an application to genetics and semantics.

A P Masucci1, A Kalampokis, V M Eguíluz

  • 1Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), E-07122 Palma de Mallorca, Spain.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 17, 2011
PubMed
Summary

We developed a new method using Jensen-Shannon divergence to map directional information flow between populations. This approach reveals genetic flow in seagrass and semantic connections across knowledge domains.

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Area of Science:

  • Ecology
  • Information Theory
  • Computational Biology

Background:

  • Understanding information flow is crucial in ecological and semantic systems.
  • Existing methods may not capture directional dynamics effectively.
  • Symbolic attributes of population elements require specialized analysis.

Purpose of the Study:

  • To introduce a generalizable method for inferring directional information flow.
  • To apply the method to diverse systems like biological populations and semantic networks.
  • To demonstrate the utility of Jensen-Shannon divergence and Shannon entropy in network inference.

Main Methods:

  • Utilizing Jensen-Shannon divergence and Shannon entropy to quantify information flow.
  • Representing population elements as n-dimensional vectors of symbolic attributes.
  • Developing network inference algorithms based on information-theoretic measures.

Main Results:

  • Successfully extracted the genetic flow network between Poseidonia oceanica seagrass meadows using microsatellite markers.
  • Constructed a semantic flow network from Wikipedia pages, illustrating knowledge domain connections.
  • Demonstrated the method's versatility across biological and digital information systems.

Conclusions:

  • The proposed method provides a robust framework for analyzing directional information transfer.
  • It offers novel insights into ecological connectivity and knowledge organization.
  • This approach has broad applicability in various scientific fields requiring network analysis.