Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Outliers Detection in Networks with Missing Links

Abstract : Outliers arise in networks due to different reasons such as fraudulent behavior 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. Identifying outliers in the presence of missing links is therefore a crucial problem in network analysis. In this work, we introduce a new algorithm to detect outliers in a network that simultaneously predicts the missing links. The proposed method is statistically sound: we prove that, under fairly general assumptions, our algorithm exactly detects the outliers, and achieves the best known error for the prediction of missing links with polynomial computation cost. It is also computationally efficient: we prove sub-linear convergence of our algorithm. We provide a simulation study which demonstrates the good behavior of the algorithm in terms of outliers detection and prediction of the missing links. We also illustrate the method with an application in epidemiology, and with the analysis of a political Twitter network. The method is freely available as an R package on the Comprehensive R Archive Network.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-02386940
Contributor : Solenne Gaucher Connect in order to contact the contributor
Submitted on : Friday, November 29, 2019 - 3:11:08 PM
Last modification on : Thursday, April 22, 2021 - 1:20:03 PM

Files

main.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02386940, version 1
  • ARXIV : 1911.13122

Collections

Citation

Solenne Gaucher, Olga Klopp, Geneviève Robin. Outliers Detection in Networks with Missing Links. 2019. ⟨hal-02386940v1⟩

Share

Metrics

Record views

166

Files downloads

106