Quantifying Transient Dynamics in Heterogeneous Networks under Various Inputs

TL;DR

This paper introduces a comprehensive theoretical framework to quantify how heterogeneity in network structures and diverse inputs influence transient dynamics, improving predictions and control in complex systems like neural networks and power grids.

Contribution

It develops a novel analytical approach using Neumann series to relate transient responses to network heterogeneity and input characteristics, extending existing spectral theory.

Findings

Node-to-node propagation as cumulative effect of directed walks

Heterogeneity amplifies response strength and duration

Framework applicable to various network motifs and input types

Abstract

Understanding how transient dynamics unfold in response to localized inputs is central to predicting and controlling signal propagation in network systems, including neural processing, epidemic intervention, and power-grid resilience. Existing theoretical frameworks typically assume homogeneous network structures and constant or pulse-like inputs, overlooking how heterogeneity in structure and variety of input shape transient responses, often leading to discrepancies between theory and observation. Here, we develop a general theoretical framework that establishes quantitative relationships between the strength and timing of transient dynamics to various inputs in heterogeneous networks. Using a Neumann series expansion, we disentangle the distinct roles of self-dynamics and network structures beyond the scope of standard spectral theory, yielding intuitive and interpretable formulations. We show that node-to-node propagation can be represented as the cumulative effect of all directed walks, each weighted recursively by the self-dynamics of participating nodes. This framework further quantifies how heterogeneity, such as broad degree distributions or additional motifs, amplifies both response strength and time. Our results advance the understanding of transient dynamics across network structures and input types, extend the existing theory to more general settings, and provide practical guidance for optimizing transient responses.