@conference {1478,
	title = {A new approach for cross-silo federated learning and its privacy risks},
	booktitle = {18th International Conference on Privacy, Security and Trust, PST 2021, Auckland, New Zealand, December 13-15, 2021},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	doi = {10.1109/PST52912.2021.9647753},
	url = {https://doi.org/10.1109/PST52912.2021.9647753},
	author = {Michele Fontana and Francesca Naretto and Anna Monreale}
}
@conference {1549,
	title = {A new approach for cross-silo federated learning and its privacy risks},
	booktitle = {2021 18th International Conference on Privacy, Security and Trust (PST)},
	year = {2021},
	doi = {10.1109/PST52912.2021.9647753},
	author = {Fontana, Michele and Naretto, Francesca and Monreale, Anna}
}
@article {1047,
	title = {NDlib: a python library to model and analyze diffusion processes over complex networks},
	journal = {International Journal of Data Science and Analytics},
	volume = {5},
	number = {1},
	year = {2018},
	pages = {61{\textendash}79},
	abstract = {Nowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground. To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.},
	doi = {10.1007/s41060-017-0086-6},
	url = {https://link.springer.com/article/10.1007/s41060-017-0086-6},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@article {1020,
	title = {NDlib: a python library to model and analyze diffusion processes over complex networks},
	journal = {International Journal of Data Science and Analytics},
	year = {2017},
	pages = {1{\textendash}19},
	abstract = {Nowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground.To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@conference {1022,
	title = {NDlib: Studying Network Diffusion Dynamics},
	booktitle = {IEEE International Conference on Data Science and Advanced Analytics, DSA},
	year = {2017},
	address = {Tokyo},
	abstract = {Nowadays the analysis of diffusive phenomena occurring on top of complex networks represents a hot topic in the Social Network Analysis playground. In order to support students, teachers, developers and researchers in this work we introduce a novel simulation framework, ND LIB . ND LIB is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. Upon the diffusion library, we designed a simulation server that allows remote execution of experiments and an online visualization tool that abstract the programmatic interface and makes available the simulation platform to non-technicians.},
	doi = {https://doi.org/10.1109/DSAA.2017.6},
	url = {https://ieeexplore.ieee.org/abstract/document/8259774},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@article {956,
	title = {Never drive alone: Boosting carpooling with network analysis},
	journal = {Information Systems},
	volume = {64},
	year = {2017},
	pages = {237{\textendash}257},
	abstract = {Carpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10\% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling.},
	doi = {10.1016/j.is.2016.03.006},
	author = {Riccardo Guidotti and Mirco Nanni and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@article {957,
	title = {Next Basket Prediction using Recurring Sequential Patterns},
	journal = {arXiv preprint arXiv:1702.07158},
	year = {2017},
	abstract = {Nowadays, a hot challenge for supermarket chains is to offer personalized services for their customers. Next basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable to capture at the same time the different factors influencing the customer{\textquoteright}s decision process: co-occurrency, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to to understand the level of the customer{\textquoteright}s stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.},
	url = {https://arxiv.org/abs/1702.07158},
	author = {Riccardo Guidotti and Giulio Rossetti and Luca Pappalardo and Fosca Giannotti and Dino Pedreschi}
}
@article {1025,
	title = {Node-centric Community Discovery: From static to dynamic social network analysis},
	journal = {Online Social Networks and Media},
	volume = {3},
	year = {2017},
	pages = {32{\textendash}48},
	abstract = {Nowadays, online social networks represent privileged playgrounds that enable researchers to study, characterize and understand complex human behaviors. Social Network Analysis, commonly known as SNA, is the multidisciplinary field of research under which researchers of different backgrounds perform their studies: one of the hottest topics in such diversified context is indeed Community Discovery. Clustering individuals, whose relations are described by a networked structure, into homogeneous communities is a complex task required by several analytical processes. Moreover, due to the user-centric and dynamic nature of online social services, during the last decades, particular emphasis was dedicated to the definition of node-centric, overlapping and evolutive Community Discovery methodologies.
In this paper we provide a comprehensive and concise review of the main results, both algorithmic and analytical, we obtained in this field. Moreover, to better underline the rationale behind our research activity on Community Discovery, in this work we provide a synthetic review of the relevant literature, discussing not only methodological results but also analytical ones.},
	doi = {https://doi.org/10.1016/j.osnem.2017.10.003},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S2468696417301052},
	author = {Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@conference {822,
	title = {A novel approach to evaluate community detection algorithms on ground truth},
	booktitle = {7th Workshop on Complex Networks},
	year = {2016},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Dijon, France},
	abstract = {Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.},
	doi = {10.1007/978-3-319-30569-1_10},
	url = {http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf},
	author = {Giulio Rossetti and Luca Pappalardo and S Rinzivillo}
}
@conference {TrasartiBG09,
	title = {A new technique for sequential pattern mining under regular expressions},
	booktitle = {SEBD},
	year = {2009},
	pages = {325-332},
	author = {Roberto Trasarti and Francesco Bonchi and Bart Goethals}
}
@article {NRRT04,
	title = {\newblock{A Declarative Framework for Reasoning on Spatio-temporal Data}},
	year = {2004},
	note = {\newblock{Book chapter in Spatio-temporal databases, flexible querying and reasoning, R. de Caluwe, G. de Tr{\`e}, G. Bordogna editors, Physica Verlag }.},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/birthday/MancarellaPR02,
	title = {Negation as Failure through Abduction: Reasoning about Termination},
	booktitle = {Computational Logic: Logic Programming and Beyond},
	year = {2002},
	pages = {240-272},
	author = {Paolo Mancarella and Dino Pedreschi and Salvatore Ruggieri}
}
@article {DBLP:journals/tkde/GiannottiMNP01,
	title = {Nondeterministic, Nonmonotonic Logic Databases},
	journal = {IEEE Trans. Knowl. Data Eng.},
	volume = {13},
	number = {5},
	year = {2001},
	pages = {813-823},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@article {DBLP:journals/amai/PedreschiS97,
	title = {Non-determinism in Deductive Databases - Preface},
	journal = {Ann. Math. Artif. Intell.},
	volume = {19},
	number = {1-2},
	year = {1997},
	pages = {1-2},
	author = {Dino Pedreschi and V. S. Subrahmanian}
}
@conference {DBLP:conf/dood/GiannottiPSZ91,
	title = {Non-Determinism in Deductive Databases},
	booktitle = {DOOD},
	year = {1991},
	pages = {129-146},
	author = {Fosca Giannotti and Dino Pedreschi and Domenico Sacc{\`a} and Carlo Zaniolo}
}
