Stability Optimization and Analysis of Energy Flow Networks versus Different Centrality Measurement

TL;DR

This study systematically examines how different centrality metrics affect the stability of energy flow networks, revealing that node importance measures significantly influence control performance across various network structures.

Contribution

It introduces a unified dynamical model and comprehensive analysis of centrality impacts on network stability, filling a gap in understanding their practical effects in complex systems.

Findings

Centrality choice markedly affects stability outcomes.

Network structure influences the effectiveness of different centrality metrics.

Stability is highly sensitive to small changes in node rankings.

Abstract

Optimizing the stability and control performance of complex networks often hinges on effectively identifying critical nodes for targeted intervention. Due to their inherent complexity and high dimensionality, large-scale energy flow networks, prevalent in domains like power grids, transportation, and financial systems, present unique challenges in selecting optimal nodes for resource allocation. While numerous centrality measurements, such as Katz centrality, eigenvector centrality, closeness centrality, betweenness centrality, and PageRank, have been proposed to evaluate node importance, the impact of different centrality metrics on stability outcomes remains inadequately understood. Moreover, networks manifest diverse structural characteristics-including small-world, scale-free, and random graph properties-which further complicates the optimization problem. This paper systematically investigates how various node centrality measurements influence control stability across representative complex network structures. A unified energy-flow dynamical model is developed, and performance metrics such as the L1 norm are employed to quantify the network stability implications of employing different centrality metrics. Extensive numerical simulations over statistically generated network ensembles reveal significant variances in stability outcomes, highlighting the crucial role of centrality selection. The findings underscore the sensitivity of energy-flow stability to seemingly minor changes in topological node rankings, providing practical insights for enhancing control efficiency and robustness in real-world networked systems.