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B
A. Baroni, Conte, A., Patrignani, M., and Ruggieri, S., Efficiently Clustering Very Large Attributed Graphs, arXiv preprint arXiv:1703.08590, 2017.
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.
F. Bodria, Rinzivillo, S., Fadda, D., Guidotti, R., Giannotti, F., and Pedreschi, D., Explaining Black Box with visual exploration of Latent Space, EuroVis–Short Papers, 2022.
F. Bodria, Panisson, A., Perotti, A., and Piaggesi, S., Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis., in SEBD, 2020.
K. S. Boeg, Ira Assent,, and Magnani, M., Efficient GPU-based skyline computation, in DAMON@SIGMOD 2013, 2013.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., ExAnte: Anticipated Data Reduction in Constrained Pattern Mining, in PKDD, 2003, pp. 59-70.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint, in ICDM, 2003, pp. 11-18.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., Efficient breadth-first mining of frequent pattern with monotone constraints, Knowl. Inf. Syst., vol. 8, pp. 131-153, 2005.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., Exante: A Preprocessing Method for Frequent-Pattern Mining, IEEE Intelligent Systems, vol. 20, pp. 25-31, 2005.
C
R. Cazabet, Boudebza, S., and Rossetti, G., Evaluating community detection algorithms for progressively evolving graphs, arXiv preprint arXiv:2007.08635, 2020.
P. Cintia, Pappalardo, L., and Pedreschi, D., "Engine Matters": {A} First Large Scale Data Driven Study on Cyclists' Performance, in 13th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, TX, USA, December 7-10, 2013, 2013.
S. Citraro and Rossetti, G., Eva: Attribute-Aware Network Segmentation, in International Conference on Complex Networks and Their Applications, 2019.
P. Contucci, Panizzi, E., Ricci-Tersenghi, F., and Sirbu, A., Egalitarianism in the rank aggregation problem: a new dimension for democracy, Quality & Quantity, pp. 1–16, 2015.
L. Corbucci, Monreale, A., and Pellungrini, R., Enhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection, in Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT, 2024.
L. Corbucci, Guidotti, R., and Monreale, A., Explaining Black-Boxes in Federated Learning, in Explainable Artificial Intelligence, Cham, 2023.
L. Corciulo, Giannotti, F., Pedreschi, D., and Zaniolo, C., Expressive Power of Non-Deterministic Operators for Logic-based Languages, in Workshop on Deductive Databases and Logic Programming, 1994, pp. 27-40.
F
A. Fedele, Explain and Interpret Few-Shot Learning, in Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lisbon, Portugal, July 26-28, 2023, 2023.
A. Fedele, Guidotti, R., and Pedreschi, D., Explaining Siamese Networks in Few-Shot Learning for Audio Data, in Discovery Science - 25th International Conference, DS 2022, Montpellier, France, October 10-12, 2022, Proceedings, 2022.
N. Forgó, Hänold, S., van den Hoven, J., Krügel, T., Lishchuk, I., Mahieu, R., Monreale, A., Pedreschi, D., Pratesi, F., and van Putten, D., An ethico-legal framework for social data science, 2020.
B. Furletti, Fornasari, F., and Montanari, C., AN EXTENSIBLE AND INTERACTIVE SOFTWARE AGENT FOR MOBILE DEVICES BASED ON GPS DATA, in IADIS International Conference Applied Computing, 2008.

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