Logo image
Graph hierarchy: a novel framework to analyse hierarchical structures in complex networks
Journal article   Peer reviewed

Graph hierarchy: a novel framework to analyse hierarchical structures in complex networks

Giannis Moutsinas, Choudhry Shuaib, Weisi Guo and Stephen Jarvis
Scientific reports, Vol.11(1), pp.13943-11
06/07/2021
PMID: 34230531

Abstract

Multidisciplinary Sciences Science & Technology Science & Technology - Other Topics
Trophic coherence, a measure of a graph's hierarchical organisation, has been shown to be linked to a graph's structural and dynamical aspects such as cyclicity, stability and normality. Trophic levels of vertices can reveal their functional properties, partition and rank the vertices accordingly. Trophic levels and hence trophic coherence can only be defined on graphs with basal vertices, i.e. vertices with zero in-degree. Consequently, trophic analysis of graphs had been restricted until now. In this paper we introduce a hierarchical framework which can be defined on any simple graph. Within this general framework, we develop several metrics: hierarchical levels, a generalisation of the notion of trophic levels, influence centrality, a measure of a vertex's ability to influence dynamics, and democracy coefficient, a measure of overall feedback in the system. We discuss how our generalisation relates to previous attempts and what new insights are illuminated on the topological and dynamical aspects of graphs. Finally, we show how the hierarchical structure of a network relates to the incidence rate in a SIS epidemic model and the economic insights we can gain through it.
url
https://doi.org/10.1038/s41598-021-93161-4View
Published (Version of record) Open

Metrics

1 Record Views

Details

Logo image

Usage Policy