Change-Point Detection in Dynamic Networks with Missing Links

Farida Enikeeva (1) , Olga Klopp (2, 3)

1 LMA [Poitiers] - Laboratoire de mathématiques et applications [UMR 7348]
2 ESSEC Business School
3 CREST - Centre de Recherche en Économie et Statistique

Structural changes occur in dynamic networks quite frequently and its detection is an important question in many situations such as fraud detection or cybersecurity. Real-life networks are often incompletely observed due to individual non-response or network size. In the present paper we consider the problem of change-point detection at a temporal sequence of partially observed networks. The goal is to test whether there is a change in the network parameters. Our approach is based on the Matrix CUSUM test statistic and allows growing size of networks. We show that the proposed test is minimax optimal and robust to missing links. We also demonstrate the good behavior of our approach in practice through simulation study and a real-data application.

Domaines: Statistiques [math.ST]

Farida Enikeeva, Olga Klopp. Change-Point Detection in Dynamic Networks with Missing Links. 2021. ⟨hal-03271921v1⟩