MONILE is a tutorial on the research field of "Temporal Networks" from different viewpoints:
- Modeling and representing temporal networks
- Mining temporal networks and dynamic graphs
- Machine learning on temporal networks
Currently, the first edition of this tutorial is hosted at the 4th Italian Conference on Big Data and Data Science - ITADATA2025
- Berlingerio, M., Bonchi, F., Bringmann, B., and Gionis, A. Mining graph evolution rules. In joint European conference on machinelearning and knowledge discovery in databases (2009), Springer, pp. 115–130.
- Scharwächter, E., Müller, E., Donges, J., Hassani, M., and Seidl, T. Detecting change processes in dynamic networks by frequent graphevolution rule mining. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (2016), IEEE, pp. 1191–1196.
- Leung, C., Lim, E.-P., Lo, D., and Weng, J. Mining interesting link formation rules in social networks. pp. 209–218.
- Yuuki, M., Ozaki, T., and Takenao, O. Mining interesting patterns and rules in a time-evolving graph. Lecture Notes in Engineering andComputer Science 2188 (03 2011)
- Vaculík, K. A versatile algorithm for predictive graph rule mining. In ITAT (2015), pp. 51–58
- Gauvin, Laetitia, et al. "Randomized reference models for temporal networks." SIAM Review 64.4 (2022): 763-830.
- Galdeman, Alessia, Matteo Zignani, and Sabrina Gaito. "Unfolding temporal networks through statistically significant graph evolutionrules.” 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2023.
- Galdeman, Alessia, Matteo Zignani, and Sabrina Gaito. "Disentangling the Growth of Blockchain-based Networks by Graph EvolutionRule Mining." 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2022.