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S. Ruggieri and Turini, F., A KDD process for discrimination discovery, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2016.
S. Ruggieri, Enumerating Distinct Decision Trees, in International Conference on Machine Learning, 2017.
S. Ruggieri, Introduction to the special issue on Artificial Intelligence for Society and Economy, Intelligenza Artificiale, vol. 9, pp. 23–23, 2015.
S. Ruggieri, Hajian, S., Kamiran, F., and Zhang, X., Anti-discrimination analysis using privacy attack strategies, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2014.
S. Ruggieri, Using t-closeness anonymity to control for non-discrimination., Trans. Data Privacy, vol. 7, pp. 99–129, 2014.
S. Ruggieri, Eirinakis, P., Subramani, K., and Wojciechowski, P., On the complexity of quantified linear systems, Theoretical Computer Science, vol. 518, pp. 128–134, 2014.
S. Ruggieri and Mesnard, F., Typing Linear Constraints for Moding CLP() Programs, in SAS, 2008, pp. 128-143.
S. Ruggieri, Data Anonymity Meets Non-discrimination, in Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on, 2013.
S. Ruggieri, Learning from polyhedral sets, in Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013.
A. Rossi, Pedreschi, D., Clifton, D. A., and Morelli, D., Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts, Sensors, vol. 20, p. 7122, 2020.
A. Rossi, Pappalardo, L., Cintia, P., F Iaia, M., Fernàndez, J., and Medina, D., Effective injury forecasting in soccer with GPS training data and machine learning, PloS one, vol. 13, p. e0201264, 2018.
A. Rossi, Perri, E., Pappalardo, L., Cintia, P., and F Iaia, M., Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load, Applied Sciences, vol. 9, p. 5174, 2019.
G. Rossetti, Milli, L., and Cazabet, R., CDLIB: a python library to extract, compare and evaluate communities from complex networks, Applied Network Science, vol. 4, p. 52, 2019.
G. Rossetti, Guidotti, R., Pennacchioli, D., Pedreschi, D., and Giannotti, F., Interaction Prediction in Dynamic Networks exploiting Community Discovery, in International conference on Advances in Social Network Analysis and Mining, ASONAM 2015, Paris, France, 2015.
G. Rossetti, Citraro, S., and Milli, L., Conformity: a Path-Aware Homophily measure for Node-Attributed Networks, IEEE Intelligent SystemsIEEE Intelligent Systems, pp. 1 - 1, 2021.
G. Rossetti, Pappalardo, L., Kikas, R., Pedreschi, D., Giannotti, F., and Dumas, M., Community-centric analysis of user engagement in Skype social network, in International conference on Advances in Social Network Analysis and Mining, Paris, France, 2015.
G. Rossetti, Pappalardo, L., and Rinzivillo, S., A novel approach to evaluate community detection algorithms on ground truth, in 7th Workshop on Complex Networks, Dijon, France, 2016.
G. Rossetti, Guidotti, R., Miliou, I., Pedreschi, D., and Giannotti, F., A supervised approach for intra-/inter-community interaction prediction in dynamic social networks, Social Network Analysis and Mining, vol. 6, p. 86, 2016.
G. Rossetti, Pappalardo, L., Pedreschi, D., and Giannotti, F., Tiles: an online algorithm for community discovery in dynamic social networks, Machine Learning, vol. 106, pp. 1213–1241, 2017.
G. Rossetti, Pappalardo, L., Kikas, R., Pedreschi, D., Giannotti, F., and Dumas, M., Homophilic network decomposition: a community-centric analysis of online social services, Social Network Analysis and Mining, vol. 6, p. 103, 2016.
G. Rossetti and Cazabet, R., Community Discovery in Dynamic Networks: a Survey, Journal ACM Computing Surveys, vol. 51, 2018.
G. Rossetti, Milli, L., Giannotti, F., and Pedreschi, D., Forecasting success via early adoptions analysis: A data-driven study, PloS one, vol. 12, p. e0189096, 2017.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., NDlib: a python library to model and analyze diffusion processes over complex networks, International Journal of Data Science and Analytics, pp. 1–19, 2017.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., NDlib: Studying Network Diffusion Dynamics, in IEEE International Conference on Data Science and Advanced Analytics, DSA, Tokyo, 2017.
G. Rossetti, Pedreschi, D., and Giannotti, F., Node-centric Community Discovery: From static to dynamic social network analysis, Online Social Networks and Media, vol. 3, pp. 32–48, 2017.

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