{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.24317",
  "author_total": 5,
  "authors": [
    {
      "name": "Yongyi Wang"
    },
    {
      "name": "Eli Newby"
    },
    {
      "name": "Yang Jiao"
    },
    {
      "name": "Salvatore Torquato"
    },
    {
      "name": "Réka Albert"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/85v5xg6dv1dkv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.24317",
  "fulltext_url": null,
  "id": "85v5xg6dv1dkv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.24317",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.stat-mech"
    ],
    [
      "openalex",
      "3109|Complex Network Analysis Techniques"
    ]
  ],
  "openalex": "W7223053067",
  "pdf_url": "https://arxiv.org/pdf/2610.24317",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/85v5xg6dv1dkv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:52Z",
      "native_id": "2610.24317",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.24317"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Encoding Hyperuniformity in Geometric Networks: Structural Signatures and Diffusion Dynamics",
  "title_text": "Encoding Hyperuniformity in Geometric Networks: Structural Signatures and Diffusion Dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:41Z",
  "url": "https://research.draconitic.com/paper/85v5xg6dv1dkv",
  "venue": "arXiv",
  "versions": []
}