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Change-Point Detection in Dynamic Networks with Missing Links

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  • Farida Enikeeva

    (Laboratoire de Mathématiques et Applications, Unité Mixe de Recherche de Centre National de la Recherche Scientifique 7348, Université de Poitiers, 86073 Poitiers, France)

  • Olga Klopp

    (Ecole Supérieure de Sciences Economiques et de gestion Business School and Centre de Recherche en Economie et Statistique, Ecole Nationale de la Statistique et l’Administration Economique, 95021 Cergy-Pontoise Cedex, France)

Abstract

Structural changes occur in dynamic networks quite frequently and their detection is an important question in many situations, such as fraud detection or cybersecurity. Real-life networks are often incompletely observed because of individual nonresponse 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 cumulative sum test statistic and allows growing the 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.

Suggested Citation

  • Farida Enikeeva & Olga Klopp, 2025. "Change-Point Detection in Dynamic Networks with Missing Links," Operations Research, INFORMS, vol. 73(5), pages 2417-2429, September.
    • Handle: RePEc:inm:oropre:v:73:y:2025:i:5:p:2417-2429
    DOI: 10.1287/opre.2021.0413
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