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M. Berlingerio, Bonchi, F., and Giannotti, F., “Towards Constraint-Based Subgraph Mining”, in SEBD, 2007, pp. 274-281.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., “Evolving networks: Eras and turning points”, Intell. Data Anal., vol. 17, pp. 27–48, 2013.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., “Multidimensional networks: foundations of structural analysis”, World Wide Web, vol. Volume 15 / 2012, 2012.
M. Berlingerio, Bonchi, F., Giannotti, F., and Turini, F., “Time-Annotated Sequences for Medical Data Mining”, in ICDM Workshops, 2007, pp. 133-138.
M. Berlingerio, Bonchi, F., Bringmann, B., and Gionis, A., “Mining Graph Evolution Rules”, in ECML/PKDD 2009, Bled, Slovenia, 2009, pp. 115-130.
M. Berlingerio, Coscia, M., and Giannotti, F., “Mining the Information Propagation in a Network”, in SEBD, 2009, pp. 333-340.
M. Berlingerio, Bonchi, F., Giannotti, F., and Turini, F., “Mining Clinical Data with a Temporal Dimension: A Case Study”, in BIBM, 2007, pp. 429-436.
M. Berlingerio, Bonchi, F., Curcio, M., Giannotti, F., and Turini, F., “Mining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation”, in Biomedical Data and Applications, 2009, pp. 211-236.
M. Berlingerio, Giannotti, F., Nanni, M., and Pinelli, F., “Temporal analysis of process logs: a case study”, in SEBD, 2008, pp. 430-437.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., “Towards Discovery of Eras in Social Networks”, in M3SN 2010 Workshop, in conjunction with ICDE2010, 2010.
M. Berlingerio, Coscia, M., and Giannotti, F., “Mining the Temporal Dimension of the Information Propagation”, in IDA, 2009, pp. 237-248.
M. Berlingerio, Pinelli, F., Nanni, M., and Giannotti, F., “Temporal mining for interactive workflow data analysis”, in KDD, 2009, pp. 109-118.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., “Foundations of Multidimensional Network Analysis”, in ASONAM, 2011, pp. 485-489.
M. Berlingerio, Bicer, V., Botea, A., Braghin, S., Lopes, N., Guidotti, R., and Pratesi, F., “Mobility Mining for Journey Planning in Rome”, in Machine Learning and Knowledge Discovery in Databases, 2015.
M. Berlingerio, Coscia, M., and Giannotti, F., “Finding and Characterizing Communities in Multidimensional Networks”, in ASONAM, 2011, pp. 490-494.
M. Berlingerio, Coscia, M., and Giannotti, F., “Finding redundant and complementary communities in multidimensional networks”, in CIKM, 2011, pp. 2181-2184.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., “The pursuit of hubbiness: Analysis of hubs in large multidimensional networks”, J. Comput. Science, vol. 2, pp. 223-237, 2011.
C. Bertazzoni and Giannotti, F., “RASP: A Resource Allocator for Software Projects”, in IEA/AIE (Vol. 2), 1990, pp. 628-637.
B. Bertolino, Meo, L., Pedreschi, D., and Turini, F., The Type System of LML. 1992, pp. 313-332.
B. Bertolino, Mancarella, P., Meo, L., Nini, L., Pedreschi, D., and Turini, F., “A Progress Report on the LML Project”, in FGCS, 1988, pp. 675-684.
C. Bessiere, De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., and Pedreschi, D., “Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach.”. 2016.
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.
G. Betti, Mulas, A., Natilli, M., Neri, L., and Verma, V., “Comparative indicators of regional poverty and deprivation: Poland versus EU-15 Member States”, in conference Comparative Economic Analysis of Households‟ Behaviour (CEAHB): Old and New EU Members, Warsaw University, 2005.
F. Bodria, Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., and Rinzivillo, S., “Benchmarking and Survey of Explanation Methods for Black Box Models”, CoRR, vol. abs/2102.13076, 2021.
F. Bodria, Panisson, A., Perotti, A., and Piaggesi, S., “Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis.”, in SEBD, 2020.

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