@article {1435,
	title = {Introduction to the special issue on social mining and big data ecosystem for open, responsible data science},
	year = {2021},
	month = {2021/03/05},
	isbn = {2364-4168},
	doi = {https://link.springer.com/article/10.1007/s41060-021-00253-5},
	url = {https://doi.org/10.1007/s41060-021-00253-5},
	author = {Luca Pappalardo and Grossi, Valerio and Dino Pedreschi}
}
@article {1358,
	title = {Identifying and exploiting homogeneous communities in labeled networks},
	journal = {Applied Network Science},
	volume = {5},
	number = {1},
	year = {2020},
	pages = {1{\textendash}20},
	abstract = {Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting EVA, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate EVA on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that EVA is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better EVA: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.},
	doi = {https://doi.org/10.1007/s41109-020-00302-1},
	url = {https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-1},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@conference {1263,
	title = {Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers},
	booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Given the wide use of machine learning approaches based on opaque prediction models, understanding the reasons behind decisions of black box decision systems is nowadays a crucial topic. We address the problem of providing meaningful explanations in the widely-applied image classification tasks. In particular, we explore the impact of changing the neighborhood generation function for a local interpretable model-agnostic explanator by proposing four different variants. All the proposed methods are based on a grid-based segmentation of the images, but each of them proposes a different strategy for generating the neighborhood of the image for which an explanation is required. A deep experimentation shows both improvements and weakness of each proposed approach.},
	doi = {10.1007/978-3-030-16148-4_5},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-16148-4_5},
	author = {Riccardo Guidotti and Anna Monreale and Cariaggi, Leonardo}
}
@article {1133,
	title = {The italian music superdiversity},
	journal = {Multimedia Tools and Applications},
	year = {2018},
	pages = {1{\textendash}23},
	abstract = {Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs{\textquoteright} melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.},
	doi = {10.1007/s11042-018-6511-6},
	url = {https://link.springer.com/article/10.1007/s11042-018-6511-6},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@article {959,
	title = {ICON Loop Carpooling Show Case},
	journal = {Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach},
	volume = {10101},
	year = {2017},
	pages = {310},
	abstract = {In this chapter we describe a proactive carpooling service that combines induction and optimization mechanisms to maximize the impact of carpooling within a community. The approach autonomously infers the mobility demand of the users through the analysis of their mobility traces (i.e. Data Mining of GPS trajectories) and builds the network of all possible ride sharing opportunities among the users. Then, the maximal set of carpooling matches that satisfy some standard requirements (maximal capacity of vehicles, etc.) is computed through Constraint Programming models, and the resulting matches are proactively proposed to the users. Finally, in order to maximize the expected impact of the service, the probability that each carpooling match is accepted by the users involved is inferred through Machine Learning mechanisms and put in the CP model. The whole process is reiterated at regular intervals, thus forming an instance of the general ICON loop.},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf$\#$page=314},
	author = {Mirco Nanni and Lars Kotthoff and Riccardo Guidotti and Barry O{\textquoteright}Sullivan and Dino Pedreschi}
}
@article {958,
	title = {The Inductive Constraint Programming Loop},
	journal = {Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach},
	volume = {10101},
	year = {2017},
	pages = {303},
	abstract = {Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming (ICON) loop. In this approach data is gathered and analyzed systematically in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other end.},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf$\#$page=307},
	author = {Mirco Nanni and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Paparrizou, Anastasia and Dino Pedreschi and Simonis, Helmut}
}
@article {955,
	title = {The Inductive Constraint Programming Loop},
	journal = {IEEE Intelligent Systems},
	year = {2017},
	abstract = {Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, which we call the inductive constraint programming loop. In this approach data is gathered and analyzed systematically in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other.},
	doi = {10.1109/MIS.2017.265115706},
	author = {Bessiere, Christian and De Raedt, Luc and Tias Guns and Lars Kotthoff and Mirco Nanni and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Paparrizou, Anastasia and Dino Pedreschi and Simonis, Helmut}
}
@conference {1019,
	title = {Information diffusion in complex networks: The active/passive conundrum},
	booktitle = {International Workshop on Complex Networks and their Applications},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Ideas, information, viruses: all of them, with their mechanisms, can spread over the complex social tissues described by our interpersonal relations. Classical spreading models can agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such simplification makes easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, partial, simulation outcomes. In this work we discuss the concepts of active and passive diffusion: moving from analysis of a well-known passive model, the Threshold one, we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation. Our data-driven analysis shows how, in such context, the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches.},
	doi = {10.1007/978-3-319-72150-7_25},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-72150-7_25},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@conference {816,
	title = {Interaction Prediction in Dynamic Networks exploiting Community Discovery},
	booktitle = {International conference on Advances in Social Network Analysis and Mining, ASONAM 2015},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris, France},
	abstract = {Due to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.},
	isbn = {978-1-4503-3854-7},
	doi = {0.1145/2808797.2809401},
	url = {http://dl.acm.org/citation.cfm?doid=2808797.2809401},
	author = {Giulio Rossetti and Riccardo Guidotti and Diego Pennacchioli and Dino Pedreschi and Fosca Giannotti}
}
@article {980,
	title = {Introduction to the special issue on Artificial Intelligence for Society and Economy},
	journal = {Intelligenza Artificiale},
	volume = {9},
	number = {1},
	year = {2015},
	pages = {23{\textendash}23},
	doi = {10.3233/IA-150074},
	author = {Salvatore Ruggieri}
}
@proceedings {825,
	title = {ItEM: A Vector Space Model to Bootstrap an Italian Emotive Lexicon},
	volume = {II},
	year = {2015},
	abstract = {In  recent  years  computational  linguistics  has  seen  a  rising  interest  in subjectivity,  opinions,  feelings and  emotions.  Even  though  great attention  has been given to polarity recognition, the research in emotion detection has had to rely on small emotion resources. In this paper,  we  present  a  methodology  to  build emotive   lexicons   by  jointly   exploiting vector  space  models  and  human  annotation,  and  we  provide  the  first  results  of the  evaluation  with  a  crowdsourcing  experiment.},
	isbn = {978-88-99200-62-6},
	author = {Lucia Passaro and Pollacci, Laura and Lenci, Alessandro}
}
@article {986,
	title = {Introduction to special issue on computational methods for enforcing privacy and fairness in the knowledge society},
	journal = {Artificial Intelligence and Law},
	volume = {22},
	number = {2},
	year = {2014},
	pages = {109{\textendash}111},
	doi = {10.1007/s10506-014-9153-7},
	author = {Sergio Mascetti and Ricci, Annarita and Salvatore Ruggieri}
}
@conference {ideas/LiraRRTT14,
	title = {Investigating semantic regularity of human mobility lifestyle},
	booktitle = {18th International Database Engineering {\&} Applications Symposium, {IDEAS} 2014, Porto, Portugal, July 7-9, 2014},
	year = {2014},
	pages = {314{\textendash}317},
	publisher = {ACM},
	organization = {ACM},
	address = {Porto, Portugal},
	doi = {10.1145/2628194.2628226},
	url = {http://doi.acm.org/10.1145/2628194.2628226},
	author = {Vinicius Monteiro de Lira and S Rinzivillo and Chiara Renso and Val{\'e}ria Ces{\'a}rio Times and Patr{\'{\i}}cia C. A. R. Tedesco}
}
@conference {537,
	title = {Inferring human activities from GPS tracks UrbComp},
	booktitle = {Workshop at KDD 2013},
	year = {2013},
	address = {Chicago USA},
	author = {Paolo Cintia and Barbara Furletti and Chiara Renso}
}
@conference {487,
	title = {Identifying users profiles from mobile calls habits},
	booktitle = {ACM SIGKDD International Workshop on Urban Computing},
	year = {2012},
	publisher = {ACM New York, NY, USA {\textcopyright}2012},
	organization = {ACM New York, NY, USA {\textcopyright}2012},
	address = {Beijing, China},
	abstract = {The huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.},
	isbn = {978-1-4503-1542-5},
	doi = {10.1145/2346496.2346500},
	url = {http://delivery.acm.org/10.1145/2350000/2346500/p17-furletti.pdf?ip=146.48.83.121\&acc=ACTIVE\%20SERVICE\&CFID=166768290\&CFTOKEN=58719386\&__acm__=1357648050_e23771c2f6bd8feb96bd66b39294175d},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@conference {686,
	title = {Individual Mobility Profiles: Methods and Application on Vehicle Sharing},
	booktitle = {Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings},
	year = {2012},
	url = {http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb},
	author = {Roberto Trasarti and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {569,
	title = {Injecting Discrimination and Privacy Awareness Into Pattern Discovery},
	booktitle = {12th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, Brussels, Belgium, December 10, 2012},
	year = {2012},
	pages = {360{\textendash}369},
	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. Data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a combined pattern sanitization framework that yields both privacy and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion.},
	doi = {10.1109/ICDMW.2012.51},
	url = {http://dx.doi.org/10.1109/ICDMW.2012.51},
	author = {Sara Hajian and Anna Monreale and Dino Pedreschi and Josep Domingo-Ferrer and Fosca Giannotti}
}
@article {798,
	title = {Integrating heterogeneous gene expression data for gene regulatory network modelling.},
	journal = {Theory Biosci},
	volume = {131},
	year = {2012},
	month = {2012 Jun},
	pages = {95-102},
	abstract = {<p>Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors{\textquoteright} knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.</p>},
	issn = {1611-7530},
	doi = {10.1007/s12064-011-0133-0},
	author = {Alina Sirbu and Heather J Ruskin and Martin Crane}
}
@conference {1277,
	title = {The impact of wine and food tourism in Italy: an analysis of official statistical data at province level},
	booktitle = {First European Conference on Wine and Food Tourism},
	year = {2011},
	author = {Michela Natilli and Romano, Maria Francesca}
}
@article {473,
	title = {Improving the Business Plan Evaluation Process: the Role of Intangibles},
	journal = {Quality Technology \& Quantitative Management},
	volume = {7},
	number = {1},
	year = {2010},
	month = {2010},
	chapter = {35},
	issn = {1684-3703},
	url = {http://web.it.nctu.edu.tw/~qtqm/upcomingpapers/2010V7N1/2010V7N1_F3.pdf},
	author = {Barbara Furletti and Franco Turini and Andrea Bellandi and Miriam Baglioni and Chiara Pratesi}
}
@conference {PedreschiRT09,
	title = {Integrating induction and deduction for finding evidence of discrimination},
	booktitle = {ICAIL},
	year = {2009},
	pages = {157-166},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@conference {DBLP:conf/kdid/BonchiGLOPT06,
	title = {On Interactive Pattern Mining from Relational Databases},
	booktitle = {KDID},
	year = {2006},
	pages = {42-62},
	author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@conference {DBLP:conf/sebd/LuccheseBGOPT06,
	title = {On Interactive Pattern Mining from Relational Databases},
	booktitle = {SEBD},
	year = {2006},
	pages = {329-338},
	author = {Claudio Lucchese and Francesco Bonchi and Fosca Giannotti and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@booklet {1271,
	title = {Indicators of social exclusion and poverty in Europe{\textquoteright}s regions},
	year = {2005},
	author = {Verma, Vijay and Betti, Gianni and Michela Natilli and Lemmi, Achille}
}
@article {MRRT03,
	title = {Integrating Knowledge Representation and Reasoning in Geographical},
	year = {2004},
	note = {information systems. {\em International Journal of GIS,Vol 18 (4), June }.},
	author = {Paolo Mancarella and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/gis/MancarellaRRT04,
	title = {Integrating knowledge representation and reasoning in Geographical Information Systems},
	journal = {International Journal of Geographical Information Science},
	volume = {18},
	number = {4},
	year = {2004},
	pages = {417-447},
	author = {Paolo Mancarella and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/iticse/AlfonsiSPS04,
	title = {IT4PS: information technology for problem solving},
	booktitle = {ITiCSE},
	year = {2004},
	pages = {241},
	author = {C. Alfonsi and Nello Scarabottolo and Dino Pedreschi and Maria Simi}
}
@conference {DBLP:conf/kdid/Giannotti02,
	title = {Invited talk: Logical Data Mining Query Languages},
	booktitle = {KDID},
	year = {2002},
	pages = {1},
	author = {Fosca Giannotti}
}
@conference {DBLP:conf/sebd/GiannottiMNPT99,
	title = {Integration of Deduction and Induction for Mining Supermarket Sales Data},
	booktitle = {SEBD},
	year = {1999},
	pages = {117-131},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/plilp/BrogiCMMPRT94,
	title = {Implementations of Program Composition Operations},
	booktitle = {PLILP},
	year = {1994},
	pages = {292-307},
	author = {Antonio Brogi and A. Chiarelli and Paolo Mancarella and V. Mazzotta and Dino Pedreschi and Chiara Renso and Franco Turini}
}
@article {BCMMPRT94,
	title = {Implementations of Program Composition Operations},
	year = {1994},
	note = {Programming Language Implementation and Logic Programming Lecture Notes in Computer Science, volume 844,},
	pages = {292{\textendash}307},
	author = {Antonio Brogi and A. Chiarelli and Paolo Mancarella and V. Mazzotta and Dino Pedreschi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/plilp/BrogiCMMPRT94,
	title = {Implementations of Program Composition Operations},
	booktitle = {PLILP},
	year = {1994},
	pages = {292-307},
	author = {Antonio Brogi and A. Chiarelli and Paolo Mancarella and V. Mazzotta and Dino Pedreschi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/tapsoft/BarbutiMPT87,
	title = {Intensional Negation of Logic Programs: Examples and Implementation Techniques},
	booktitle = {TAPSOFT, Vol.2},
	year = {1987},
	pages = {96-110},
	author = {Roberto Barbuti and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
