Encoding Hyperuniformity in Geometric Networks: Structural Signatures and Diffusion Dynamics

Abstract

Disordered hyperuniformity is a recently discovered exotic state exhibiting anomalously suppressed density fluctuations at long wavelengths, and its generalization to network systems remains an important open question. Here, we show that, across a wide range of systems, geometric networks retain substantially more long-wavelength density information from the underlying point configurations than Delaunay triangulations. Within geometric networks, hyperuniformity manifests as suppressed vertex-to-vertex fluctuations in a range of network structural observables, including closeness centrality, vertex strength, and eigenvector centrality. These measures not only distinguish hyperuniform networks from non-hyperuniform ones, but also separate near-hyperuniform configurations from hyperuniform systems with strong local disorder, solving a central challenge in network hyperuniformity. We connect these structural signatures to network dynamics by introducing a relaxation-time centrality based on diffusion and demonstrate that hyperuniform geometric networks exhibit more homogeneous relaxation behavior than non-hyperuniform geometric networks. These results reveal how the suppression of large-scale density fluctuations manifests in both network structure and diffusion dynamics.