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Author Title Type [ Year(Asc)]
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.
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.
G. Rossetti and Cazabet, R., Community Discovery in Dynamic Networks: a Survey, arXiv preprint arXiv:1707.03186, 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.
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 GOODTECHS 2017, Pisa, Italy, 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.
M. Nanni, Kotthoff, L., Guidotti, R., O'Sullivan, B., and Pedreschi, D., ICON Loop Carpooling Show Case, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach, vol. 10101, p. 310, 2017.
M. Nanni, Nijssen, S., O'Sullivan, B., Paparrizou, A., Pedreschi, D., and Simonis, H., The Inductive Constraint Programming Loop, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach, vol. 10101, p. 303, 2017.
C. Bessiere, De Raedt, L., Guns, T., Kotthoff, L., Nanni, M., Nijssen, S., O'Sullivan, B., Paparrizou, A., Pedreschi, D., and Simonis, H., The Inductive Constraint Programming Loop, IEEE Intelligent Systems, 2017.
L. Milli, Rossetti, G., Pedreschi, D., and Giannotti, F., Information diffusion in complex networks: The active/passive conundrum, in International Workshop on Complex Networks and their Applications, 2017.
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. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., Market Basket Prediction using User-Centric Temporal Annotated Recurring Sequences, in 2017 IEEE International Conference on Data Mining (ICDM), 2017.
R. Trasarti, Guidotti, R., Monreale, A., and Giannotti, F., MyWay: Location prediction via mobility profiling, Information Systems, vol. 64, pp. 350–367, 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.

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