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Author Title Type [ Year(Asc)]
2018
G. Rossetti and Cazabet, R., “Community Discovery in Dynamic Networks: a Survey”, Journal ACM Computing Surveys, vol. 51, 2018.
L. Milli, Rossetti, G., Pedreschi, D., and Giannotti, F., “Diffusive Phenomena in Dynamic Networks: a data-driven study”, in International Conference on Complex Networks CompleNet, Boston March 5-8 2018, 2018.
L. Gabrielli, Fadda, D., Rossetti, G., Nanni, M., Piccinini, L., Pedreschi, D., Giannotti, F., and Lattarulo, P., “Discovering Mobility Functional Areas: A Mobility Data Analysis Approach”, in International Workshop on Complex Networks, 2018.
R. Guidotti, Gabrielli, L., Monreale, A., Pedreschi, D., and Giannotti, F., “Discovering temporal regularities in retail customers’ shopping behavior”, EPJ Data Science, vol. 734701723532821246166531956119, 2018.
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.
L. Pollacci, Guidotti, R., Rossetti, G., Giannotti, F., and Pedreschi, D., “The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis”, in International Conference on Smart Objects and Technologies for Social Good, 2018.
G. Amato, Candela, L., Castelli, D., Esuli, A., Falchi, F., Gennaro, C., Giannotti, F., Monreale, A., Nanni, M., Pagano, P., Pappalardo, L., Pedreschi, D., Pratesi, F., Rabitti, F., Rinzivillo, S., Rossetti, G., Ruggieri, S., Sebastiani, F., and Tesconi, M., “How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science”, in A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, Springer International Publishing, 2018, pp. 287–306.
G. Rossetti, Milli, L., Rinzivillo, S., Sırbu, 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.
F. Giannotti, Trasarti, R., Bontcheva, K., and Grossi, V., “SoBigData: Social Mining & Big Data Ecosystem”, in Companion of the The Web Conference 2018 on The Web Conference 2018, 2018.
R. Guidotti, Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D., “A survey of methods for explaining black box models”, ACM Computing Surveys (CSUR), vol. 51, p. 93, 2018.
2017
M. Atzmueller, Becker, M., Molino, A., Mueller, J., Peters, J., and Sirbu, A., “Applications for Environmental Sensing in EveryAware”, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 135–155.
B. Dong, Wang, H. Wendy, Monreale, A., Pedreschi, D., Giannotti, F., and Guo, W., “Authenticated Outlier Mining for Outsourced Databases”, IEEE Transactions on Dependable and Secure Computing, 2017.
R. Guidotti, Monreale, A., Nanni, M., Giannotti, F., and Pedreschi, D., “Clustering Individual Transactional Data for Masses of Users”, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017.
R. Pellungrini, Pappalardo, L., Pratesi, F., and Monreale, A., “A Data Mining Approach to Assess Privacy Risk in Human Mobility Data”, ACM Trans. Intell. Syst. Technol., vol. 9, pp. 31:1–31:27, 2017.
L. Pappalardo and Simini, F., “Data-driven generation of spatio-temporal routines in human mobility”, Data Mining and Knowledge Discovery, 2017.
B. Furletti, Trasarti, R., Cintia, P., and Gabrielli, L., “Discovering and Understanding City Events with Big Data: The Case of Rome”, Information, vol. 8, p. 74, 2017.
A. Baroni, Conte, A., Patrignani, M., and Ruggieri, S., “Efficiently Clustering Very Large Attributed Graphs”, arXiv preprint arXiv:1703.08590, 2017.
P. Mukala, Cerone, A., and Turini, F., “An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free$\backslash$libre open source software (FLOSS) communities”, Education and Information Technologies, pp. 1–23, 2017.
P. Mukala, Cerone, A., and Turini, F., “An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities”, Education and Information Technologies, vol. 22, pp. 3207–3229, 2017.
S. Ruggieri, “Enumerating Distinct Decision Trees”, in International Conference on Machine Learning, 2017.
R. Guidotti and Coscia, M., “On the Equivalence Between Community Discovery and Clustering”, in International Conference on Smart Objects and Technologies for Social Good, 2017.
P. Gravino, Sirbu, A., Becker, M., Servedio, V. D. P., and Loreto, V., “Experimental Assessment of the Emergence of Awareness and Its Influence on Behavioral Changes: The Everyaware Lesson”, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 337–362.
R. Pellungrini, Pappalardo, L., Pratesi, F., and Monreale, A., “Fast Estimation of Privacy Risk in Human Mobility Data”. 2017.
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.
L. Candela, Manghi, P., Giannotti, F., Grossi, V., and Trasarti, R., “HyWare: a HYbrid Workflow lAnguage for Research E-infrastructures”, D-Lib Magazine, vol. 23, 2017.

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