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Journal Article
D. Pedreschi, Ruggieri, S., and Smaus, J. - G., “Classes of terminating logic programs”, TPLP, vol. 2, pp. 369-418, 2002.
M. Coscia, Giannotti, F., and Pedreschi, D., “A classification for community discovery methods in complex networks”, Statistical Analysis and Data Mining, vol. 4, pp. 512-546, 2011.
K. R. Apt, Gabbrielli, M., and Pedreschi, D., “A Closer Look at Declarative Interpretations”, J. Log. Program., vol. 28, pp. 147-180, 1996.
S. Marina Joseph, Citraro, S., Morini, V., Rossetti, G., and Stella, M., “Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities”, arXiv preprint arXiv:2110.15269, 2021.
A. Sirbu, Loreto, V., Servedio, V. D. P., and Tria, F., “Cohesion, consensus and extreme information in opinion dynamics”, Advances in Complex Systems, vol. 16, p. 1350035, 2013.
G. Rossetti and Cazabet, R., “Community Discovery in Dynamic Networks: a Survey”, Journal ACM Computing Surveys, vol. 51, 2018.
A. Sirbu, Ruskin, H. J., and Crane, M., “Comparison of evolutionary algorithms in gene regulatory network model inference.”, BMC Bioinformatics, vol. 11, p. 59, 2010.
P. Mancarella, Martini, S., and Pedreschi, D., “Complete Logic Programs with Domain-Closure Axiom”, J. Log. Program., vol. 5, pp. 263-276, 1988.
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.
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, Citraro, S., and Milli, L., “Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks”, arXiv preprint arXiv:2012.05195, 2020.
V. Bogorny, Renso, C., de Aquino, A. R., de Siqueira, F. L., and Alvares, L. O., “CONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects ”, Transaction in GIS, 2013.
F. Bonchi, Giannotti, F., Lucchese, C., Orlando, S., Perego, R., and Trasarti, R., “A constraint-based querying system for exploratory pattern discovery”, Inf. Syst., vol. 34, pp. 3-27, 2009.
F. Bonchi, Giannotti, F., Lucchese, C., Orlando, S., Perego, R., and Trasarti, R., “A constraint-based querying system for exploratory pattern discovery”, Inf. Syst., vol. 34, pp. 3-27, 2009.
A. Sirbu, Ruskin, H. J., and Crane, M., “Cross-platform microarray data normalisation for regulatory network inference.”, PLoS One, vol. 5, p. e13822, 2010.
A. Monreale, Trasarti, R., Pedreschi, D., Renso, C., and Bogorny, V., “C-safety: a framework for the anonymization of semantic trajectories”, Transactions on Data Privacy, vol. 4, pp. 73-101, 2011.
A. Sirbu, Crane, M., and Ruskin, H. J., “Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks”, Microarrays, vol. 4, pp. 255–269, 2015.
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.
D. Janssens, Giannotti, F., Nanni, M., Pedreschi, D., and Rinzivillo, S., “Data Science for Simulating the Era of Electric Vehicles”, KI - Künstliche Intelligenz, 2012.
L. Pappalardo and Simini, F., “Data-driven generation of spatio-temporal routines in human mobility”, Data Mining and Knowledge Discovery, 2017.
F. Giannotti and Pedreschi, D., “Datalog with Non-Deterministic Choice Computers NDB-PTIME”, J. Log. Program., vol. 35, pp. 79-101, 1998.
M. Aldinucci, Ruggieri, S., and Torquati, M., “Decision tree building on multi-core using FastFlow”, Concurrency and Computation: Practice and Experience, vol. 26, pp. 800–820, 2014.
S. Bruestle, Pappalardo, L., and Guidotti, R., “Defining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data”, Available at SSRN 3452058, 2019.
C. R. Daniel Ornellana, “Developing a Spatial Knowledge Representation for Pedestrian Interactions”, 2009.
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.

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