@conference {1609,
	title = {From GPS Traces to Individual Emission Exposure: A Data-Driven Four-Step Process},
	booktitle = {Intelligent Transport Systems},
	year = {2025},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	address = {Cham},
	isbn = {978-3-031-86370-7},
	doi = {10.1007/978-3-031-86370-7_5},
	author = {Aliyev, Gurban and Nanni, Mirco}
}
@article {1550,
	title = {Fair Federated Learning methodology based on Multi-Objective Optimization},
	journal = {Submitted at JAIR},
	year = {2023},
	author = {Michele Fontana, Francesca Naretto, Anna Monreale, Mirco Nanni, Fosca Giannotti}
}
@conference {1485,
	title = {From Mean-Field to Complex Topologies: Network Effects on the Algorithmic Bias Model},
	booktitle = {Complex Networks \& Their Applications X},
	year = {2022},
	author = {Valentina Pansanella and Giulio Rossetti and Letizia Milli}
}
@article {1283,
	title = {Factual and Counterfactual Explanations for Black Box Decision Making},
	journal = {IEEE Intelligent Systems},
	year = {2019},
	abstract = {The rise of sophisticated machine learning models has brought accurate but obscure decision systems, which hide their logic, thus undermining transparency, trust, and the adoption of artificial intelligence (AI) in socially sensitive and safety-critical contexts. We introduce a local rule-based explanation method, providing faithful explanations of the decision made by a black box classifier on a specific instance. The proposed method first learns an interpretable, local classifier on a synthetic neighborhood of the instance under investigation, generated by a genetic algorithm. Then, it derives from the interpretable classifier an explanation consisting of a decision rule, explaining the factual reasons of the decision, and a set of counterfactuals, suggesting the changes in the instance features that would lead to a different outcome. Experimental results show that the proposed method outperforms existing approaches in terms of the quality of the explanations and of the accuracy in mimicking the black box.},
	doi = {10.1109/MIS.2019.2957223},
	url = {https://ieeexplore.ieee.org/abstract/document/8920138},
	author = {Riccardo Guidotti and Anna Monreale and Fosca Giannotti and Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@conference {1039,
	title = {The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Nowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a {\textquotedblleft}fractal{\textquotedblright} musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians{\textquoteright} popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.},
	doi = {10.1007/978-3-319-76111-4_19},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@booklet {993,
	title = {Fast Estimation of Privacy Risk in Human Mobility Data},
	year = {2017},
	abstract = {Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual{\textquoteright}s mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods. },
	isbn = {978-3-319-66283-1},
	doi = {10.1007/978-3-319-66284-8_35 },
	author = {Roberto Pellungrini and Luca Pappalardo and Francesca Pratesi and Anna Monreale}
}
@article {1018,
	title = {Forecasting success via early adoptions analysis: A data-driven study},
	journal = {PloS one},
	volume = {12},
	number = {12},
	year = {2017},
	pages = {e0189096},
	abstract = {Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don{\textquoteright}t. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.},
	author = {Giulio Rossetti and Letizia Milli and Fosca Giannotti and Dino Pedreschi}
}
@conference {1129,
	title = {The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Nowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a {\textquotedblleft}fractal{\textquotedblright} musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians{\textquoteright} popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.

},
	doi = {https://doi.org/10.1007/978-3-319-76111-4_19},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {760,
	title = {Find Your Way Back: Mobility Profile Mining with Constraints},
	booktitle = {Principles and Practice of Constraint Programming},
	year = {2015},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cork},
	abstract = {Mobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.},
	author = {Lars Kotthoff and Mirco Nanni and Riccardo Guidotti and Barry O{\textquoteright}Sullivan}
}
@conference {566,
	title = {Fair pattern discovery},
	booktitle = {Symposium on Applied Computing, {SAC} 2014, Gyeongju, Republic of Korea - March 24 - 28, 2014},
	year = {2014},
	pages = {113{\textendash}120},
	abstract = {Data mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. We are assisting to unprecedented opportunities of understanding human and society behavior that unfortunately is darkened by several risks for human rights: one of this is the unfair discrimination based on the extracted patterns and profiles. Consider the case when a set of patterns extracted from the personal data of a population of individual persons is released for subsequent use in a decision making process, such as, e.g., granting or denying credit. Decision rules based on such patterns may lead to unfair discrimination, depending on what is represented in the training cases. In this context, we address the discrimination risks resulting from publishing frequent patterns. We present a set of pattern sanitization methods, one for each discrimination measure used in the legal literature, for fair (discrimination-protected) publishing of frequent pattern mining results. Our proposed pattern sanitization methods yield discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. Finally, the effectiveness of our proposals is assessed by extensive experiments.},
	doi = {10.1145/2554850.2555043},
	url = {http://doi.acm.org/10.1145/2554850.2555043},
	author = {Sara Hajian and Anna Monreale and Dino Pedreschi and Josep Domingo-Ferrer and Fosca Giannotti}
}
@conference {asonam22011,
	title = {Finding and Characterizing Communities in Multidimensional Networks},
	booktitle = {ASONAM},
	year = {2011},
	pages = {490-494},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {cikm2011,
	title = {Finding redundant and complementary communities in multidimensional networks},
	booktitle = {CIKM},
	year = {2011},
	pages = {2181-2184},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {asonam12011,
	title = {Foundations of Multidimensional Network Analysis},
	booktitle = {ASONAM},
	year = {2011},
	pages = {485-489},
	abstract = {Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens inmonodimensional network, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we develop a solid repertoire of basic concepts and analytical measures, which takes into account the general structure of multidimensional networks. We tested our framework on a real world multidimensional network, showing the validity and the meaningfulness of the measures introduced, that are able to extract important, nonrandom, information about complex phenomena.},
	doi = {10.1109/ASONAM.2011.103},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@proceedings {358,
	title = {From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data},
	year = {2011},
	author = {Gennady Andrienko and Natalia Andrienko and Cristophe Hurter and S Rinzivillo and Stefan Wrobel}
}
@conference {DBLP:conf/sebd/BoschiGP04,
	title = {Frequent Pattern Queries for Flexible Knowledge Discovery},
	booktitle = {SEBD},
	year = {2004},
	pages = {250-261},
	author = {Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@article {DBLP:journals/amai/FayzullinNPS00,
	title = {Foundations of distributed interaction systems},
	journal = {Ann. Math. Artif. Intell.},
	volume = {28},
	number = {1-4},
	year = {2000},
	pages = {127-168},
	author = {Marat Fayzullin and Mirco Nanni and Dino Pedreschi and V. S. Subrahmanian}
}
