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

TL;DR

This paper presents a versatile method for inferring directed information flow networks from population data, demonstrated through genetic flow in seagrass meadows and semantic flow between Wikipedia topics.

Contribution

It introduces a novel approach based on Jensen-Shannon divergence and Shannon entropy for inferring directional networks from symbolic attribute data.

Findings

Successfully extracted genetic flow network in seagrass populations.

Revealed semantic channels between different knowledge areas.

Method applicable to diverse domains with symbolic data.

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.