{
  "abstract": "We propose a novel and flexible nonlinear approach for dimensionality reduction of large-scale multiview network data and derive its theory. The (linear) predictor incorporates observed covariates (edge-specific, layer-specific, and global) and Gaussian latent factors. Inference is conducted via the graph Laplace approximated maximum likelihood estimator. Letting $K$ denote the number of network layers and $n_V$ the number of nodes, we derive asymptotic theory under two regimes: (i) $K \\to \\infty$ with fixed $n_V$, and (ii) double asymptotics $K, n_V \\to \\infty$, establishing consistency and asymptotic normality for local and global parameters, with distinct convergence rates. In an application to the gravity model for commodity trades, we use a zero-adjusted Gamma distribution with latent factors and observable covariates (e.g.\\ distance, tariffs, common language) to capture excess zeros, skewness, and unobserved heterogeneity. Synthetic and real-data exercises show that our approach outperforms the routinely applied Poisson pseudo-maximum likelihood estimator with fixed effects. We complement our theoretical and empirical contributions with open-source R/C++ routines and a novel strategy for starting values selection.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.26495",
  "author_total": 3,
  "authors": [
    {
      "name": "Anna van Es"
    },
    {
      "name": "Eva Cantoni"
    },
    {
      "name": "Davide La Vecchia"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0ertandxa4y2c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.26495",
  "fulltext_url": null,
  "id": "0ertandxa4y2c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.26495",
  "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",
      "stat.ME"
    ],
    [
      "openalex",
      "3109|Complex Network Analysis Techniques"
    ]
  ],
  "openalex": "W7214127069",
  "pdf_url": "https://arxiv.org/pdf/2609.26495",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0ertandxa4y2c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:49Z",
      "native_id": "2609.26495",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.26495"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Flexible latent variable models on graphs: Laplace approximated inference for multiview network data",
  "title_text": "Flexible latent variable models on graphs: Laplace approximated inference for multiview network data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:49Z",
  "url": "https://research.draconitic.com/paper/0ertandxa4y2c",
  "venue": "arXiv",
  "versions": []
}