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.
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.
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.
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