Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "16048"
  #_source: array:26 [
    "id" => "16048"
    "slug" => "16048-denoising-over-networks-with-applications-to-partially-observed-epidemics"
    "yearMonth" => "2026-03"
    "year" => "2026"
    "title" => "Denoising over networks with applications to partially observed epidemics"
    "description" => "DONNAT, C., KLOPP, O. et VERZELEN, N. (2026). Denoising over networks with applications to partially observed epidemics. <i>Computational Statistics and Data Analysis</i>, 215, pp. 108276."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "KLOPP Olga"
        "bid" => "B00732676"
        "slug" => "klopp-olga"
      ]
      1 => array:1 [
        "name" => "DONNAT Claire"
      ]
      2 => array:1 [
        "name" => "VERZELEN Nicolas"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "High-dimensional statistics"
      1 => "Graph total variation"
      2 => "Graph trend filtering "
      3 => "Epidemic forecasting"
      4 => "Epidemic nowcasting"
    ]
    "updatedAt" => "2025-10-20 11:47:14"
    "publicationUrl" => "https://www.sciencedirect.com/science/article/abs/pii/S0167947325001525?via%3Dihub"
    "publicationInfo" => array:3 [
      "pages" => "108276"
      "volume" => "215"
      "number" => ""
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "A novel method is introduced for denoising partially observed signals over networks using graph total variation (TV) regularization, a technique adapted from signal processing to handle binary data. This approach extends existing results derived for Gaussian data to the discrete, binary case — a method hereafter referred to as “one-bit TV denoising.” The framework considers a network represented as a set of nodes with binary observations, where edges encode pairwise relationships between nodes. A key theoretical contribution is the establishment of consistency guarantees of graph TV denoising for the recovery of underlying node-level probabilities. The method is well suited for settings with missing data, enabling robust inference from incomplete observations. Extensive numerical experiments and real-world applications further highlight its effectiveness, underscoring its potential in various practical scenarios that require denoising and prediction on networks with binary-valued data. Finally, applications to two real-world epidemic scenarios demonstrate that one-bit total variation denoising significantly enhances the accuracy of network-based nowcasting and forecasting."
      "en" => "A novel method is introduced for denoising partially observed signals over networks using graph total variation (TV) regularization, a technique adapted from signal processing to handle binary data. This approach extends existing results derived for Gaussian data to the discrete, binary case — a method hereafter referred to as “one-bit TV denoising.” The framework considers a network represented as a set of nodes with binary observations, where edges encode pairwise relationships between nodes. A key theoretical contribution is the establishment of consistency guarantees of graph TV denoising for the recovery of underlying node-level probabilities. The method is well suited for settings with missing data, enabling robust inference from incomplete observations. Extensive numerical experiments and real-world applications further highlight its effectiveness, underscoring its potential in various practical scenarios that require denoising and prediction on networks with binary-valued data. Finally, applications to two real-world epidemic scenarios demonstrate that one-bit total variation denoising significantly enhances the accuracy of network-based nowcasting and forecasting."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2025-12-06T05:21:43.000Z"
    "docTitle" => "Denoising over networks with applications to partially observed epidemics"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/klopp-olga">KLOPP Olga</a>, DONNAT Claire, VERZELEN Nicolas"
    "docDescription" => "<span class="document-property-authors">KLOPP Olga, DONNAT Claire, VERZELEN Nicolas</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2026</span>"
    "keywordList" => "<a href="#">High-dimensional statistics</a>, <a href="#">Graph total variation</a>, <a href="#">Graph trend filtering </a>, <a href="#">Epidemic forecasting</a>, <a href="#">Epidemic nowcasting</a>"
    "docPreview" => "<b>Denoising over networks with applications to partially observed epidemics</b><br><span>2026-03 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://www.sciencedirect.com/science/article/abs/pii/S0167947325001525?via%3Dihub" target="_blank">Denoising over networks with applications to partially observed epidemics</a>"
  ]
  +lang: "en"
  +"_score": 8.714403
  +"_ignored": array:2 [
    0 => "abstract.en.keyword"
    1 => "abstract.fr.keyword"
  ]
  +"parent": null
}