@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 {1429,
	title = {Conformity: a Path-Aware Homophily measure for Node-Attributed Networks},
	journal = {IEEE Intelligent SystemsIEEE Intelligent Systems},
	year = {2021},
	month = {2021},
	pages = {1 - 1},
	abstract = {Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.},
	isbn = {1941-1294},
	doi = {https://doi.org/10.1109/MIS.2021.3051291},
	url = {https://ieeexplore.ieee.org/document/9321348},
	author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli}
}
@article {1364,
	title = {Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks},
	journal = {arXiv preprint arXiv:2012.05195},
	year = {2020},
	author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli}
}
@conference {1365,
	title = {Opinion Dynamic Modeling of Fake News Perception},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2020},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Fake news diffusion represents one of the most pressing issues of our online society. In recent years, fake news has been analyzed from several points of view, primarily to improve our ability to separate them from the legit ones as well as identify their sources. Among such vast literature, a rarely discussed theme is likely to play uttermost importance in our understanding of such a controversial phenomenon: the analysis of fake news{\textquoteright} perception. In this work, we approach such a problem by proposing a family of opinion dynamic models tailored to study how specific social interaction patterns concur to the acceptance, or refusal, of fake news by a population of interacting individuals. To discuss the peculiarities of the proposed models, we tested them on several synthetic network topologies, thus underlying when/how they affect the stable states reached by the performed simulations.},
	doi = {https://doi.org/10.1007/978-3-030-65347-7_31},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-65347-7_31},
	author = {Toccaceli, Cecilia and Letizia Milli and Giulio Rossetti}
}
@article {1367,
	title = {UTLDR: an agent-based framework for modeling infectious diseases and public interventions},
	journal = {arXiv preprint arXiv:2011.05606},
	year = {2020},
	author = {Giulio Rossetti and Letizia Milli and Salvatore Citraro and Morini, Virginia}
}
@article {1407,
	title = {CDLIB: a python library to extract, compare and evaluate communities from complex networks},
	journal = {Applied Network Science},
	volume = {4},
	year = {2019},
	month = {2019/07/29},
	pages = {52},
	abstract = {Community Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library - namely CDlib - designed to serve this need. The aim of CDlib is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.},
	isbn = {2364-8228},
	doi = {https://doi.org/10.1007/s41109-019-0165-9},
	url = {https://link.springer.com/article/10.1007/s41109-019-0165-9},
	author = {Giulio Rossetti and Letizia Milli and Cazabet, R{\'e}my}
}
@conference {1372,
	title = {Community-Aware Content Diffusion: Embeddednes and Permeability},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {Letizia Milli and Giulio Rossetti}
}
@article {1179,
	title = {Active and passive diffusion processes in complex networks},
	journal = {Applied network science},
	volume = {3},
	number = {1},
	year = {2018},
	pages = {42},
	abstract = {Ideas, information, viruses: all of them, with their mechanisms, spread over the complex social information, viruses: all tissues described by our interpersonal relations. Usually, to simulate and understand the unfolding of such complex phenomena are used general mathematical models; these models act agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such degree of abstraction makes it easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, incorrect, simulation outcomes. In this work we introduce the concepts of active and passive diffusion to discriminate the degree in which individuals choice affect the overall spreading of content over a social graph. Moving from the analysis of a well-known passive diffusion schema, the Threshold model (that can be used to model peer-pressure related processes), we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation.

Our analysis, performed both in synthetic and real-world data, underline that the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches to capture the real complexity of the simulated system better.},
	doi = {https://doi.org/10.1007/s41109-018-0100-5},
	url = {https://link.springer.com/article/10.1007/s41109-018-0100-5},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@conference {1021,
	title = {Diffusive Phenomena in Dynamic Networks: a data-driven study},
	booktitle = {International Conference on Complex Networks CompleNet},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	address = {Boston March 5-8 2018},
	abstract = {Everyday, ideas, information as well as viruses spread over complex social tissues described by our interpersonal relations. So far, the network contexts upon which diffusive phenomena unfold have usually considered static, composed by a fixed set of nodes and edges. Recent studies describe social networks as rapidly changing topologies. In this work {\textendash} following a data-driven approach {\textendash} we compare the behaviors of classical spreading models when used to analyze a given social network whose topological dynamics are observed at different temporal-granularities. Our goal is to shed some light on the impacts that the adoption of a static topology has on spreading simulations as well as to provide an alternative formulation of two classical diffusion models.},
	doi = {10.1007/978-3-319-73198-8_13},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_13},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@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 {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 {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}
}
@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}
}
@conference {820,
	title = {Quantification in Social Networks},
	booktitle = {International Conference on Data Science and Advanced Analytics (IEEE DSAA{\textquoteright}2015)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris, France},
	abstract = {In many real-world applications there is a need to monitor the distribution of a population across different classes, and to track changes in this distribution over time. As an example, an important task is to monitor the percentage of unemployed adults in a given region. When the membership of an individual in a class cannot be established deterministically, a typical solution is the classification task. However, in the above applications the final goal is not determining which class the individuals belong to, but estimating the prevalence of each class in the unlabeled data. This task is called quantification. Most of the work in the literature addressed the quantification problem considering data presented in conventional attribute format. Since the ever-growing availability of web and social media we have a flourish of network data representing a new important source of information and by using quantification network techniques we could quantify collective behavior, i.e., the number of users that are involved in certain type of activities, preferences, or behaviors. In this paper we exploit the homophily effect observed in many social networks in order to construct a quantifier for networked data. Our experiments show the effectiveness of the proposed approaches and the comparison with the existing state-of-the-art quantification methods shows that they are more accurate. },
	doi = {10.1109/DSAA.2015.7344845},
	url = {http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/main_DSAA.pdf},
	author = {Letizia Milli and Anna Monreale and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti and Fabrizio Sebastiani}
}
@conference {573,
	title = {Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach},
	booktitle = {47th SIS Scientific Meeting of the Italian Statistica Society},
	year = {2014},
	month = {06/2014},
	address = {Cagliari },
	abstract = {The Big Data, originating from the digital breadcrumbs of human activi-
ties, sensed as a by-product of the technologies that we use for our daily activities, let
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data {\textquotedblleft}proxies{\textquotedblright}, as the mobile
calls data for mobility. In this paper we investigate to what extent such {\textquotedblright}big data{\textquotedblright},
in integration with administrative ones, could be a support in producing reliable and
timely estimates of inter-city mobility. The study has been jointly developed by Is-
tat, CNR, University of Pisa in the range of interest of the {\textquotedblleft}Commssione di studio
avente il compito di orientare le scelte dellIstat sul tema dei Big Data {\textquotedblright}. In an on-
going project at ISTAT, called {\textquotedblleft}Persons and Places{\textquotedblright} {\textendash} based on an integration of
administrative data sources, it has been produced a first release of Origin Destina-
tion matrix {\textendash} at municipality level {\textendash} assuming that the places of residence and that
of work (or study) be the terminal points of usual individual mobility for work or
study. The coincidence between the city of residence and that of work (or study) {\textendash}
is considered as a proxy of the absence of intercity mobility for a person (we define
him a static resident). The opposite case is considered as a proxy of presence of mo-
bility (the person is a dynamic resident: commuter or embedded). As administrative
data do not contain information on frequency of the mobility, the idea is to specify
an estimate method, using calling data as support, to define for each municipality the
stock of standing residents, embedded city users and daily city users (commuters)},
	isbn = {978-88-8467-874-4},
	url = {http://www.sis2014.it/proceedings/allpapers/3026.pdf},
	author = {Barbara Furletti and Lorenzo Gabrielli and Fosca Giannotti and Letizia Milli and Mirco Nanni and Dino Pedreschi}
}
@conference {563,
	title = {Quantification Trees},
	booktitle = {2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013},
	year = {2013},
	pages = {528{\textendash}536},
	abstract = {In many applications there is a need to monitor how a population is distributed across different classes, and to track the changes in this distribution that derive from varying circumstances, an example such application is monitoring the percentage (or "prevalence") of unemployed people in a given region, or in a given age range, or at different time periods. When the membership of an individual in a class cannot be established deterministically, this monitoring activity requires classification. However, in the above applications the final goal is not determining which class each individual belongs to, but simply estimating the prevalence of each class in the unlabeled data. This task is called quantification. In a supervised learning framework we may estimate the distribution across the classes in a test set from a training set of labeled individuals. However, this may be sub optimal, since the distribution in the test set may be substantially different from that in the training set (a phenomenon called distribution drift). So far, quantification has mostly been addressed by learning a classifier optimized for individual classification and later adjusting the distribution it computes to compensate for its tendency to either under-or over-estimate the prevalence of the class. In this paper we propose instead to use a type of decision trees (quantification trees) optimized not for individual classification, but directly for quantification. Our experiments show that quantification trees are more accurate than existing state-of-the-art quantification methods, while retaining at the same time the simplicity and understandability of the decision tree framework.},
	doi = {10.1109/ICDM.2013.122},
	url = {http://dx.doi.org/10.1109/ICDM.2013.122},
	author = {Letizia Milli and Anna Monreale and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi and Fabrizio Sebastiani}
}
