KLOPP Olga, GAUCHER Solenne, ROBIN Geneviève
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.
GAUCHER, S., KLOPP, O. et ROBIN, G. (2021). Outlier detection in networks with missing links. Computational Statistics and Data Analysis, 164, pp. 107308.