00391nas a2200085   4500008004100000245007800041210006900119100000500188856011200193       2023                        eng d00aFair Federated Learning methodology based on Multi-Objective Optimization0 aFair Federated Learning methodology based on MultiObjective Opti1 a  uhttps://kdd.isti.cnr.it/publications/fair-federated-learning-methodology-based-multi-objective-optimization00485nas a2200109   4500008004100000245008900041210006900130100002600199700002100225700001900246856011000265       2022                        eng d00aFrom Mean-Field to Complex Topologies: Network Effects on the Algorithmic Bias Model0 aFrom MeanField to Complex Topologies Network Effects on the Algo1 aPansanella, Valentina1 aRossetti, Giulio1 aMilli, Letizia  uhttps://kdd.isti.cnr.it/publications/mean-field-complex-topologies-network-effects-algorithmic-bias-model01554nas a2200157   4500008004100000245007400041210006900115520102800184100002301212700001901235700002101254700002001275700002401295700001901319856005801338       2019                        eng d00aFactual and Counterfactual Explanations for Black Box Decision Making0 aFactual and Counterfactual Explanations for Black Box Decision M3 aThe 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.1 aGuidotti, Riccardo1 aMonreale, Anna1 aGiannotti, Fosca1 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://ieeexplore.ieee.org/abstract/document/892013801229nas a2200157   4500008004100000245008100041210006900122260001300191520069500204100002000899700002300919700002100942700002100963700002000984856006701004       2018                        eng d00aThe Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis0 aFractal Dimension of Music Geography Popularity and Sentiment An  bSpringer3 aNowadays 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 “fractal” 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’ 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.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1901483nas a2200145   4500008004100000020002200041245005900063210005900122520097800181100002501159700002101184700002301205700001901228856009001247       2017                        eng d  a978-3-319-66283-100aFast Estimation of Privacy Risk in Human Mobility Data0 aFast Estimation of Privacy Risk in Human Mobility Data3 aMobility 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’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. 1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttps://kdd.isti.cnr.it/publications/fast-estimation-privacy-risk-human-mobility-data01579nas a2200157   4500008004100000245007400041210006900115300001300184490000700197520103200204100002101236700001901257700002101276700002001297856010401317       2017                        eng d00aForecasting success via early adoptions analysis: A data-driven study0 aForecasting success via early adoptions analysis A datadriven st  ae01890960 v123 aInnovations 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’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.1 aRossetti, Giulio1 aMilli, Letizia1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/forecasting-success-early-adoptions-analysis-data-driven-study01239nas a2200157   4500008004100000245008100041210006900122260001900191520069900210100002000909700002300929700002100952700002100973700002000994856006701014       2017                        eng d00aThe Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis0 aFractal Dimension of Music Geography Popularity and Sentiment An  bSpringer, Cham3 aNowadays 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 “fractal” 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’ 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.

1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1901284nas a2200145   4500008004100000245006500041210006400106260004400170520074700214100001900961700001700980700002300997700002201020856009601042       2015                        eng d00aFind Your Way Back: Mobility Profile Mining with Constraints0 aFind Your Way Back Mobility Profile Mining with Constraints  aCorkbSpringer International Publishing3 aMobility 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.1 aKotthoff, Lars1 aNanni, Mirco1 aGuidotti, Riccardo1 aO'Sullivan, Barry  uhttps://kdd.isti.cnr.it/publications/find-your-way-back-mobility-profile-mining-constraints01701nas a2200157   4500008003900000245002700039210002700066300001400093520128600107100001701393700001901410700002001429700002601449700002101475856004701496       2014                          d00aFair pattern discovery0 aFair pattern discovery  a113–1203 aData 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.1 aHajian, Sara1 aMonreale, Anna1 aPedreschi, Dino1 aDomingo-Ferrer, Josep1 aGiannotti, Fosca  uhttp://doi.acm.org/10.1145/2554850.255504300486nas a2200121   4500008003900000245007200039210006900111300001200180100002500192700002000217700002100237856010600258       2011                          d00aFinding and Characterizing Communities in Multidimensional Networks0 aFinding and Characterizing Communities in Multidimensional Netwo  a490-4941 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-and-characterizing-communities-multidimensional-networks00506nas a2200121   4500008003900000245008100039210006900120300001400189100002500203700002000228700002100248856011500269       2011                          d00aFinding redundant and complementary communities in multidimensional networks0 aFinding redundant and complementary communities in multidimensio  a2181-21841 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-redundant-and-complementary-communities-multidimensional-networks01804nas a2200157   4500008003900000245005300039210005300092300001200145520129700157100002501454700002001479700002101499700001901520700002001539856008701559       2011                          d00aFoundations of Multidimensional Network Analysis0 aFoundations of Multidimensional Network Analysis  a485-4893 aComplex 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.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/foundations-multidimensional-network-analysis00594nas a2200133   4500008003900000245011900039210006900158100002300227700002300250700002200273700001800295700001900313856012800332       2011                          d00aFrom Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data0 aFrom Movement Tracks through Events to Places Extracting and Cha1 aAndrienko, Gennady1 aAndrienko, Natalia1 aHurter, Cristophe1 aRinzivillo, S1 aWrobel, Stefan  uhttps://kdd.isti.cnr.it/publications/movement-tracks-through-events-places-extracting-and-characterizing-significant-places00452nas a2200121   4500008004100000245006200041210006200103300001200165100002200177700002100199700002000220856009000240       2004                        eng d00aFrequent Pattern Queries for Flexible Knowledge Discovery0 aFrequent Pattern Queries for Flexible Knowledge Discovery  a250-2611 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/frequent-pattern-queries-flexible-knowledge-discovery00468nas a2200145   4500008004100000245005100041210005100092300001200143490000700155100002100162700001700183700002000200700002200220856008000242       2000                        eng d00aFoundations of distributed interaction systems0 aFoundations of distributed interaction systems  a127-1680 v281 aFayzullin, Marat1 aNanni, Mirco1 aPedreschi, Dino1 aSubrahmanian, V S  uhttps://kdd.isti.cnr.it/content/foundations-distributed-interaction-systems