Effective Graph Resistance as Cumulative Heat Dissipation

TL;DR

This paper reveals a direct relationship between effective graph resistance and heat dissipation in Laplacian dynamics, offering a physically intuitive and multi-scale approach to analyze and optimize network connectivity.

Contribution

It establishes an exact, physically transparent link between effective graph resistance and heat dissipation, enabling efficient network optimization strategies beyond combinatorial complexity.

Findings

Effective graph resistance equals total heat dissipation during Laplacian relaxation.

Multi-scale decomposition of the Laplacian spectrum informs network modifications.

New continuous strategies for network optimization are introduced.

Abstract

Effective graph resistance is a fundamental structural metric in network science, widely used to quantify global connectivity, compare network architectures, and assess robustness in flow-based systems. Despite its importance, current formulations rely mainly on spectral or pseudo-inverse Laplacian representations, offering limited physical insight into how structural features shape this quantity or how it can be efficiently optimized. Here, we establish an exact and physically transparent relationship between effective graph resistance and the cumulative heat dissipation generated by Laplacian diffusion dynamics. We show that the total heat dissipated during relaxation to equilibrium precisely equals the effective graph resistance. This dynamical viewpoint uncovers a natural multi-scale decomposition of the Laplacian spectrum: early-time dissipation is governed by degree-based local structure, intermediate times isolate eigenvalues below the spectral mean, and long times are dominated by the algebraic connectivity. These multi-scale properties yield continuous and interpretable strategies for modifying network structure and constructing optimized ensembles, enabling improvements that are otherwise NP-hard to achieve via combinatorial methods. Our results unify structural and dynamical perspectives on network connectivity and provide new tools for analyzing, comparing, and optimizing complex networks across domains.