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2021
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.
F. Lillo and Ruggieri, S., “Estimating the Total Volume of Queries to a Search Engine”, IEEE Transactions on Knowledge and Data Engineering, pp. 1-1, 2021.
M. Nanni, Andrienko, G., Barabasi, A. - L., Boldrini, C., Bonchi, F., Cattuto, C., Chiaromonte, F., Comandé, G., Conti, M., Coté, M., Dignum, F., Dignum, V., Domingo-Ferrer, J., Ferragina, P., Giannotti, F., Guidotti, R., Helbing, D., Kaski, K., Kertész, J., Lehmann, S., Lepri, B., Lukowicz, P., Matwin, S., Jiménez, D. Megías, Monreale, A., Morik, K., Oliver, N., Passarella, A., Passerini, A., Pedreschi, D., Pentland, A., Pianesi, F., Pratesi, F., Rinzivillo, S., Ruggieri, S., Siebes, A., Torra, V., Trasarti, R., van den Hoven, J., and Vespignani, A., “Give more data, awareness and control to individual citizens, and they will help COVID-19 containment”, 2021.
M. Setzu, Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., and Giannotti, F., “GLocalX - From Local to Global Explanations of Black Box AI Models”, vol. 294, p. 103457, 2021.
L. Pappalardo, Grossi, V., and Pedreschi, D., “Introduction to the special issue on social mining and big data ecosystem for open, responsible data science”, 2021.
G. Cornacchia and Pappalardo, L., “STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together”, 2021.
2020
M. Natilli, Fadda, D., Rinzivillo, S., Pedreschi, D., and Licari, F., “Analysis and Visualization of Performance Indicators in University Admission Tests”, in Formal Methods. FM 2019 International Workshops, Cham, 2020.
G. Rossetti, “ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks”, Applied Network Science, vol. 5, pp. 1–23, 2020.
D. Pedreschi and Miliou, I., “Artificial Intelligence (AI): new developments and innovations applied to e-commerce”, European Parliament's committee on the Internal Market and Consumer Protection, 2020.
B. Dong, Wang, H., Monreale, A., Pedreschi, D., Giannotti, F., and Guo, W., “Authenticated Outlier Mining for Outsourced Databases”, IEEE Transactions on Dependable and Secure Computing, vol. 17, pp. 222 - 235, 2020.
E. Ntoutsi, Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. - E., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., and others, “Bias in data-driven artificial intelligence systems—An introductory survey”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, p. e1356, 2020.
R. Guidotti, Monreale, A., Matwin, S., and Pedreschi, D., “Black Box Explanation by Learning Image Exemplars in the Latent Feature Space”, in Machine Learning and Knowledge Discovery in Databases, Cham, 2020.
G. Rossetti, Morini, V., and Pollacci, L., “Capturing Political Polarization of Reddit Submissions in the Trump Era”, in SEBD, 2020.
B. Qureshi, Kamiran, F., Karim, A., Ruggieri, S., and Pedreschi, D., “Causal inference for social discrimination reasoning”, vol. 54, pp. 425 - 437, 2020.
G. Rossetti, Citraro, S., and Milli, L., “Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks”, arXiv preprint arXiv:2012.05195, 2020.
J. Kim, Sirbu, A., Giannotti, F., and Gabrielli, L., “Digital Footprints of International Migration on Twitter”, in International Symposium on Intelligent Data Analysis, 2020.
C. Panigutti, Perotti, A., and Pedreschi, D., “Doctor XAI: an ontology-based approach to black-box sequential data classification explanations”, in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020.
A. Rossi, Pedreschi, D., Clifton, D. A., and Morelli, D., “Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts”, Sensors, vol. 20, p. 7122, 2020.
V. Voukelatou, Pappalardo, L., Gabrielli, L., and Giannotti, F., “Estimating countries’ peace index through the lens of the world news as monitored by GDELT”, in 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020.
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.
R. Cazabet, Boudebza, S., and Rossetti, G., “Evaluating community detection algorithms for progressively evolving graphs”, arXiv preprint arXiv:2007.08635, 2020.
O. Lampridis, Guidotti, R., and Ruggieri, S., “Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars”, in Discovery Science, Cham, 2020.
M. Setzu, Guidotti, R., Monreale, A., and Turini, F., “Global Explanations with Local Scoring”, in Machine Learning and Knowledge Discovery in Databases, Cham, 2020.
A. Sirbu, Andrienko, G., Andrienko, N., Boldrini, C., Conti, M., Giannotti, F., Guidotti, R., Bertoli, S., Kim, J., Muntean, C. Ioana, Pappalardo, L., Passarella, A., Pedreschi, D., Pollacci, L., Pratesi, F., and Sharma, R., “Human migration: the big data perspective”, International Journal of Data Science and Analytics, pp. 1–20, 2020.
S. Citraro and Rossetti, G., “Identifying and exploiting homogeneous communities in labeled networks”, Applied Network Science, vol. 5, pp. 1–20, 2020.

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