Centrality measures and the role of non-normality for network control energy reduction

TL;DR

This paper explores how non-normality in networks affects control energy and proposes driver node selection strategies based on Gramian centrality measures and network non-normality to optimize control energy.

Contribution

It introduces novel driver node selection strategies that incorporate network non-normality and Gramian-based centrality measures for improved control energy efficiency.

Findings

Non-normality relates to control energy requirements.

Proposed strategies balance average and worst-case energy.

Network non-normality impacts driver node effectiveness.

Abstract

Combinations of Gramian-based centrality measures are used for driver node selection in complex networks in order to simultaneously take into account conflicting control energy requirements, like minimizing the average energy needed to steer the state in any direction and the energy needed for the worst direction. The selection strategies that we propose are based on a characterization of the network non-normality, a concept we show is related to the idea of balanced realization.