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Machine Learning for Complex Networks

New preprint on sequential motifs in networks


In our recent preprint "Sequential Motifs in Observed Walks" we use higher-order graph models for causal paths to analyze patterns in time series data on networks. The study has been led by collaborators at Northeastern University in Boston, USA.

Our work contributes to a better understanding for the interesting patterns hidden in time-ordered interaction sequences and paths on networks. The preprint is available on arXiv.