Graph Hierarchy: A novel approach to understanding hierarchical structures in complex networks

TL;DR

This paper introduces a new framework for analyzing hierarchical structures in complex networks, extending trophic levels to all graphs and providing metrics for influence and feedback, with applications to epidemic modeling.

Contribution

The paper proposes a generalization of trophic levels to any simple graph and introduces influence centrality and democracy coefficient as new metrics.

Findings

Hierarchical levels can be defined on any simple graph.

Influence centrality measures a vertex's ability to affect dynamics.

Democracy coefficient quantifies overall feedback in the system.

Abstract

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 and partition and rank the vertices accordingly. Yet trophic levels and hence trophic coherence can only be defined on graphs with basal vertices, vertices with zero in-degree. Consequently, trophic analysis of graphs had been restricted until now. In this paper we introduce a novel framework, a generalisation of trophic levels, which we call hierarchical levels, that can be defined on any simple graph. Within this general framework, we develop additional metrics named influence centrality, a measure of a vertices ability to influence dynamics, and democracy coefficient, a measure of overall feedback in the system, both of which have implications for the controllability of complex systems. 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.