This repository contains the implementation of the framework described in paper: Sahasrabuddhe, R., Lambiotte, R., and Rosvall, M., 2025. Concise network models of memory dynamics reveal explainable patterns in path data. arXiv preprint arXiv:2501.08302.
The code was written in Python 3.9 and uses standard libraries such as numpy, pandas, and scipy. It uses the sklearn k-means clustering implementation and joblib for parallelising.
The airports_example.ipynb notebook contains a working example.
The synthetic_experiments.ipynb notebook contains the code to reproduce the results in Fig. 2 of the manuscript.
airport_trigrams.txt contains the transit trigrams extracted from flight itineraries.
The lazega_data folder contains the network structure of the Lazega law firm data.
The Convex Non-negative Matrix Factorisation at the core of the framework is due to Ding, C.H., Li, T. and Jordan, M.I., 2008. Convex and semi-nonnegative matrix factorizations. IEEE transactions on pattern analysis and machine intelligence, 32(1), pp.45-55.
Flight itinerary data is maintained by the U.S. Bureau of Transportation Statistics. https://www.transtats.bts.gov/DataIndex.asp
The Lazega law firm data was collected by Lazega, E., 2001. The collegial phenomenon: The social mechanisms of cooperation among peers in a corporate law partnership. Oxford University Press, USA. We accessed it on Manlio de Domenico's multilayer network repository. https://manliodedomenico.com/data.php