{
  "abstract": "This work presents an operator-based visual analytics pipeline for exploring synthetic systemic risk dynamics in financial networks. The framework is formulated as a composition of mathematical operators that sequentially transform synthetic financial observations into dynamic scientific visualizations. The pipeline consists of six operators: latent risk mapping, probabilistic score generation, financial network construction, distance-based contagion dynamics, visual encoding, and perspective projection. Together, these operators provide a modular computational structure linking nonlinear risk surfaces, time-dependent probabilistic states, network topology, and shock propagation. A reproducible implementation demonstrates the architecture through controlled synthetic experiments. Nonlinear latent risk representations are converted into probabilistic scores, embedded in a weighted financial network, and propagated via shortest-path contagion. The resulting states are then mapped into dynamic visual representations. The experiments illustrate how visualization can be treated as an explicit stage of the analytical process rather than a post-processing step. The proposed formulation does not aim to introduce new predictive models or contagion mechanisms. Instead, it offers a transparent and modular pipeline that connects generative risk modeling, network dynamics, and scientific visualization within a unified operator-based architecture. This approach supports reproducibility and facilitates the exploration and communication of complex systemic-risk processes in synthetic settings.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.22202",
  "author_total": 1,
  "authors": [
    {
      "name": "Ana Isabel Castillo Pereda"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cjzmxzhe43mzy.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.22202",
  "fulltext_url": null,
  "id": "cjzmxzhe43mzy",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.22202",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.PR"
    ],
    [
      "arxiv",
      "q-fin.RM"
    ],
    [
      "openalex",
      "1707|Data Visualization and Analytics"
    ]
  ],
  "openalex": "W7214033348",
  "pdf_url": "https://arxiv.org/pdf/2609.22202",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cjzmxzhe43mzy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:27Z",
      "native_id": "2609.22202",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.22202"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.PR",
      "subfield": "probability"
    }
  ],
  "title": "An Operator-Based Visual Analytics Pipeline for Synthetic Systemic Risk Dynamics",
  "title_text": "An Operator-Based Visual Analytics Pipeline for Synthetic Systemic Risk Dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:27Z",
  "url": "https://research.draconitic.com/paper/cjzmxzhe43mzy",
  "venue": "arXiv",
  "versions": []
}