Machine Learning for Complex Networks

    Paper accepted at The Web Conference


    What is the Markov order of paths in a network? In our latest published work, which has been accepted for publication at The Web Conference (WWW'22), we use Bayesian inference and model selection to address this important question in the modelling of time series data on networks.

    Our method is important for data scientists analyzing patterns in categorical sequence data that are subject to (partially) known constraints, e.g. click stream data or other behavioral data on the Web, information propagation in social networks, mobility trajectories, or pathway data in bioinformatics. Addressing the key challenge of model selection, our work is also relevant for the growing body of research that emphasizes the need for higher-order models in network analysis. This work has been led by our team member Luka Petrovic. A preprint is available on arXiv.