ResearchBig Data and Machine Learning
Anomaly detection in network data: a statistical approach

Anomaly detection in network data: a statistical approach

Leaders:  Malinovskaya, Otto
Year:  2021
More Link https://link.springer.com/article/10.1007/s11067-018-9430-1

In this master thesis, the student explores the network modelling and monitoring on the example of daily flights in the United States. In statistical modelling, there are Separable Temporal Exponential Random Graph Models (STERGM) that subdivide the network development into two distinct streams: the dissolution and formation of edges. Thus, the interpretation of changes in the network becomes clearer. For monitoring, the student should select two methods: one from statistical process control and another from a different field (e.g., machine learning). A comparison of their performance would conclude this thesis.

Sources

Broekel, T., & Bednarz, M. (2018). Disentangling link formation and dissolution in spatial networks: An Application of a Two-Mode STERGM to a Project-Based R&D Network in the German Biotechnology Industry. Networks and Spatial Economics, 18(3), 677-704.

Jeske, D. R., Stevens, N. T., Tartakovsky, A. G., & Wilson, J. D. (2018). Statistical methods for network surveillance. Applied Stochastic Models in Business and Industry, 34(4), 425-445. 

Requirements

1. Basic knowledge of statistical analysis

2. Interest in the statistical network data analysis

3. Good programming skills

Contact

Anna Malinovskaya (anna.malinovskaya@ikg.uni-hannover.de)

Prof. Dr. Philipp Otto (philipp.otto@ikg.uni-hannover.de)