{
  "abstract": "Networks are often a useful way to represent complex social and economic systems. Stochastic models for such networks should parsimoniously reflect the complexity while being both interpretable and tractable. The class of Exponential-family Random Graph Models (ERGMs) is very expressive and flexible in its representation of complex phenomena. However, the estimation and fitting of realistic ERGMs are hampered by the computational challenges of likelihood-based inference. Recently, a deterministic method based on a variational mean-field approximation was proposed that is much less expensive to compute than the standard simulation-based method. In this work, we show that the proposed method performs poorly for realistic models. We show that established alternatives, including MCMC-MLE, MPLE, sampled MPLE, and tapered ERGM-based inference, achieve superior accuracy and computational performance in the situations that the mean-field approximation was designed for.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Statistics & Data Science, University of California - Los Angeles, Los Angeles, California, United States of America"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Joseph Resch"
    },
    {
      "name": "Mark S. Handcock"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d8f5aexykk8j7.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0358535",
  "fulltext_url": null,
  "id": "d8f5aexykk8j7",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0358535",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3109|Complex Network Analysis Techniques"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Data visualization/Infographics/Graphs/Random graphs"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis/Social networks"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Approximation methods"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Probability theory/Probability distribution"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Probability theory/Statistical distributions"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Monte Carlo method"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical models"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Monte Carlo method"
    ],
    [
      "plos",
      "/Social sciences/Sociology/Social networks"
    ]
  ],
  "openalex": "W7220746903",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358535&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/d8f5aexykk8j7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0358535",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0358535"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:probability theory",
      "subfield": "probability"
    },
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    }
  ],
  "title": "Assessing variational likelihood estimation in network formation models",
  "title_text": "Assessing variational likelihood estimation in network formation models",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:36Z",
  "url": "https://research.draconitic.com/paper/d8f5aexykk8j7",
  "venue": "PLOS One",
  "versions": []
}