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The main topic of research is network analysis and modelling using statistical and machine learning tools. Network data are ubiquitous in finance, social media, the economy, and business. Social networks play an increasingly important role in society. For example, the Reddit community WallStreetBets hyped the stock of GameStop (GME), causing a short squeeze and extremely volatile stock prices in early 2021. Similarly, statements made on social networks such as Twitter have been shown to impact public and voter opinions. Exemplifying how social networks directly affect the economy, this project focusses on studying network data with a specific interest in modelling and understanding how networks change over time. Change detection in an online fashion is a challenging task in networks of large size, a common property of social networks.

In this research, the candidate will first investigate and develop interpretable network analysis approaches using statistical models and test them on real-life data. Then geometric deep learning approaches for the tasks like node classification, link prediction and graph classification will be developed with change detection ability. In the final phase of the project, the candidate should bring together two families of methods and design an optimized algorithm that is both interpretable and yields a high performance in network change detection.