Multidimensional dynamical centrality from Green functions in complex networks
Abstract
Conventional centrality measures provide compact descriptions of node importance, but they emphasize specific structural relations and do not directly resolve the temporal and spectral organization of dynamical perturbation responses. Building on the Green function of a linearized networked system, we develop a multidimensional framework for dynamical node characterization. From the same response function, we extract three complementary indicators---Influence, Efficiency, and Distortion---that quantify cumulative response strength, temporal rate of response, and spectral concentration, respectively, together with a contribution matrix that resolves these quantities into source--target pathways. As a numerical demonstration, we apply the framework to weighted Kuramoto--Sakaguchi dynamics on heterogeneous Barabási--Albert networks. The three indicators distinguish implanted dynamical node classes through complementary dimensions of the perturbation response, while remaining strongly coupled to conventional topological centralities. These results demonstrate how a common dynamical response can be decomposed into distinct, physically interpretable characteristics rather than represented by a single aggregate descriptor. We finally discuss the scope and limitations of the linear-response assumption and extensions to time-dependent and nonlinear regimes.
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BibTeX
@misc{wang2026multidimensional,
title = {{Multidimensional dynamical centrality from Green functions in complex networks}},
author = {Yusen Wang and Hao Yu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.29197},
eprint = {2609.29197},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.29197},
}
RIS
TY - UNPB
TI - Multidimensional dynamical centrality from Green functions in complex networks
AU - Yusen Wang
AU - Hao Yu
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.29197
UR - https://arxiv.org/abs/2609.29197
AB - Conventional centrality measures provide compact descriptions of node importance, but they emphasize specific structural relations and do not directly resolve the temporal and spectral organization of dynamical perturbation responses. Building on the Green function of a linearized networked system, we develop a multidimensional framework for dynamical node characterization. From the same response function, we extract three complementary indicators---Influence, Efficiency, and Distortion---that quantify cumulative response strength, temporal rate of response, and spectral concentration, respectively, together with a contribution matrix that resolves these quantities into source--target pathways. As a numerical demonstration, we apply the framework to weighted Kuramoto--Sakaguchi dynamics on heterogeneous Barabási--Albert networks. The three indicators distinguish implanted dynamical node classes through complementary dimensions of the perturbation response, while remaining strongly coupled to conventional topological centralities. These results demonstrate how a common dynamical response can be decomposed into distinct, physically interpretable characteristics rather than represented by a single aggregate descriptor. We finally discuss the scope and limitations of the linear-response assumption and extensions to time-dependent and nonlinear regimes.
ER -