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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, Berlingerio, M., and Giannotti, F., Scalable Link Prediction on Multidimensional Networks, in ICDM Workshops, Vancouver, 2011, pp. 979-986.
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, vol. 5, pp. 61–79, 2018.
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.
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.
S. Ruggieri, Data Anonymity Meets Non-discrimination, in Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on, 2013.
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, Learning from polyhedral sets, in Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013.
S. Ruggieri and Mesnard, F., Typing Linear Constraints for Moding CLP() Programs, in SAS, 2008, pp. 128-143.
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.
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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.
M. Setzu and Atzori, M., SPARQL Queries over Source Code, in 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), 2016.
A. Sirbu, Giannotti, F., Pedreschi, D., and Kertész, J., Public opinion and Algorithmic bias, ERCIM News, 2019.
A. Sirbu, Loreto, V., Servedio, V. D. P., and Tria, F., Opinion dynamics with disagreement and modulated information, Journal of Statistical Physics, pp. 1–20, 2013.
A. Sirbu, Becker, M., Caminiti, S., De Baets, B., Elen, B., Francis, L., Gravino, P., Hotho, A., Ingarra, S., Loreto, V., Molino, A., Mueller, J., Peters, J., Ricchiuti, F., Saracino, F., Servedio, V. D. P., Stumme, G., Theunis, J., Tria, F., and Van den Bossche, J., Participatory Patterns in an International Air Quality Monitoring Initiative., PLoS One, vol. 10, p. e0136763, 2015.
A. Sirbu and Babaoglu, O., Towards operator-less data centers through data-driven, predictive, proactive autonomics, Cluster Computing, pp. 1–14, 2016.
A. Sirbu, Kerr, G., Crane, M., and Ruskin, H. J., RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering., PLoS One, vol. 7, p. e50986, 2012.
A. Sirbu, Andrienko, G., Andrienko, N., Boldrini, C., Conti, M., Giannotti, F., Guidotti, R., Bertoli, S., Kim, J., Muntean, C. Ioana, and others, Human migration: the big data perspective, International Journal of Data Science and Analytics, pp. 1–20, 2020.

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