Essec\Faculty\Model\Contribution {#2216
#_index: "academ_contributions"
#_id: "12662"
#_source: array:26 [
"id" => "12662"
"slug" => "outlier-detection-in-networks-with-missing-links"
"yearMonth" => "2021-12"
"year" => "2021"
"title" => "Outlier detection in networks with missing links"
"description" => "GAUCHER, S., KLOPP, O. et ROBIN, G. (2021). Outlier detection in networks with missing links. <i>Computational Statistics and Data Analysis</i>, 164, pp. 107308."
"authors" => array:3 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "GAUCHER Solenne"
]
2 => array:1 [
"name" => "ROBIN Geneviève"
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "Outlier detection"
1 => "Robust network estimation"
2 => "Missing observations"
3 => "Link prediction"
]
"updatedAt" => "2023-01-27 01:00:40"
"publicationUrl" => "https://www.sciencedirect.com/science/article/pii/S0167947321001420?via%3Dihub"
"publicationInfo" => array:3 [
"pages" => "107308"
"volume" => "164"
"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" => "Outliers arise in networks due to different reasons such as fraudulent behaviour of malicious users or default in measurement instruments and can significantly impair network analyses. In addition, real-life networks are likely to be incompletely observed, with missing links due to individual non-response or machine failures. Therefore, identifying outliers in the presence of missing links is a crucial problem in network analysis. A new algorithm is introduced to detect outliers in a network and simultaneously predict the missing links. The proposed method is statistically sound: under fairly general assumptions, this algorithm exactly detects the outliers, and achieves the best known error for the prediction of missing links with polynomial computational cost."
"en" => "Outliers arise in networks due to different reasons such as fraudulent behaviour of malicious users or default in measurement instruments and can significantly impair network analyses. In addition, real-life networks are likely to be incompletely observed, with missing links due to individual non-response or machine failures. Therefore, identifying outliers in the presence of missing links is a crucial problem in network analysis. A new algorithm is introduced to detect outliers in a network and simultaneously predict the missing links. The proposed method is statistically sound: under fairly general assumptions, this algorithm exactly detects the outliers, and achieves the best known error for the prediction of missing links with polynomial computational cost."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-12-14T12:21:42.000Z"
"docTitle" => "Outlier detection in networks with missing links"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/klopp-olga">KLOPP Olga</a>, GAUCHER Solenne, ROBIN Geneviève"
"docDescription" => "<span class="document-property-authors">KLOPP Olga, GAUCHER Solenne, ROBIN Geneviève</span><br><span class="document-property-authors_fields">Information Systems, Decision Sciences and Statistics</span> | <span class="document-property-year">2021</span>"
"keywordList" => "<a href="#">Outlier detection</a>, <a href="#">Robust network estimation</a>, <a href="#">Missing observations</a>, <a href="#">Link prediction</a>"
"docPreview" => "<b>Outlier detection in networks with missing links</b><br><span>2021-12 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://www.sciencedirect.com/science/article/pii/S0167947321001420?via%3Dihub" target="_blank">Outlier detection in networks with missing links</a>"
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.482425
+"parent": null
}