{
  "abstract": "Complex networks balance metric wiring costs against functional routing demands, rarely conforming to idealized dichotomies of pure planar grids or unconstrained topologies. Yet, existing latent-space models remain computationally bottlenecked by stochastic sampling on small, unweighted graphs. Here, we formulate a scalable generative framework to solve the inverse problem of spatio-functional inference in weighted networks. By coupling explicit metric spaces with an unobserved latent geometry, we jointly reconstruct hidden coordinates and infer a coupling parameter $λ\\in [0, 1]$, quantifying the connectivity propensity explained by functional affinity conditioned on metric distance decay. Our deterministic pipeline pairs a physics-informed spectral residual initialization with hybrid first- and quasi-Newton optimization. Across diverse empirical systems, the framework reveals a continuous spatio-functional spectrum. In the Drosophila melanogaster whole-brain connectome, it resolves an architectural progression from metric-constrained sensory circuits to specialized associative and chemo-affinity neuropils. In the continental European power grid, line pruning preserves a strictly metric baseline ($\\hatλ \\approx 0$), whereas topological rewiring drives a sharp surge in $\\hatλ$, demonstrating that macroscopic component connectivity can mask spatial degradation. Finally, tracking global aviation throughout the COVID-19 pandemic captures dynamic regime shifts toward localized metric connectivity, establishing a unified diagnostic to probe structural organization and resilience in spatially embedded networks.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.23933",
  "author_total": 2,
  "authors": [
    {
      "name": "Gabriele Cerioli"
    },
    {
      "name": "Adamo Cerioli"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/chexrdcb9x86h.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.23933",
  "fulltext_url": null,
  "id": "chexrdcb9x86h",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.23933",
  "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",
      "physics.soc-ph"
    ],
    [
      "openalex",
      "3109|Complex Network Analysis Techniques"
    ]
  ],
  "openalex": "W7214012965",
  "pdf_url": "https://arxiv.org/pdf/2609.23933",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/chexrdcb9x86h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.23933",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.23933"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.soc-ph",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Inferring Latent Geometries in Weighted Spatio-Functional Networks",
  "title_text": "Inferring Latent Geometries in Weighted Spatio-Functional Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/chexrdcb9x86h",
  "venue": "arXiv",
  "versions": []
}