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

Abstract

We introduce a general method to infer the directional information flow between populations whose elements are described by n-dimensional vectors of symbolic attributes. The method is based on the Jensen-Shannon divergence and on the Shannon entropy and has a wide range of application. We show here the results of two applications: first extracting the network of genetic flow between the meadows of the seagrass Poseidonia Oceanica, where the meadow elements are specified by sets of microsatellite markers, then we extract the semantic flow network from a set of Wikipedia pages, showing the semantic channels between different areas of knowledge.

References in corpus (6)

• The diameter of the world wide web
• Properties of Classical and Quantum Jensen-Shannon Divergence
• Random planar graphs and the London street network
• Network analysis identifies weak and strong links in a metapopulation system
• Modeling the emergence of universality in color naming patterns
• Spectrum of genetic diversity and networks of clonal organisms

Cited by in corpus (5)

• Text authorship identified using the dynamics of word co-occurrence networks
• Wikipedia information flow analysis reveals the scale-free architecture of the Semantic Space
• Genetic flow directionality and geographical segregation in a Cymodocea nodosa genetic diversity network
• Entropy-growth-based model of emotionally charged online dialogues
• Statistical Properties of Links of Network: A Survey on the Shipping Lines of Worldwide Marine Transport Network