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V. D. P. Servedio, Caminiti, S., Gravino, P., Loreto, V., Sirbu, A., and Tria, F., “Large Scale Engagement Through Web-Gaming and Social Computations”, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 237–254.
R. Portocarre Sarmento, Lemos, L., Cordeiro, M., Rossetti, G., and Cardoso, D., “DynComm R Package–Dynamic Community Detection for Evolving Networks”, arXiv preprint arXiv:1905.01498, 2019.
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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, 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.
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.
G. Rossetti, Milli, L., Citraro, S., and Morini, V., “UTLDR: an agent-based framework for modeling infectious diseases and public interventions”, arXiv preprint arXiv:2011.05606, 2020.
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, 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, 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, “ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks”, Applied Network Science, vol. 5, pp. 1–23, 2020.
G. Rossetti, Morini, V., and Pollacci, L., “Capturing Political Polarization of Reddit Submissions in the Trump Era”, in SEBD, 2020.
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, “Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery”, in International Conference on Complex Networks and Their Applications, 2019.
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.
G. Rossetti, Pappalardo, L., and Pedreschi, D., “Measuring tie strength in multidimensional networks”, in SEDB 2013, 2013.
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.

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