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2021 Conference article Restricted

Opinion Dynamic Modeling of Fake News Perception
Toccaceli C., Milli L., Rossetti G.
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' 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.Source: Complex Networks 2020 - Ninth International Conference on Complex Networks and Their Applications, pp. 370–381, Online Conference, 20/12/2020
DOI: 10.1007/978-3-030-65347-7_31
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: academic.microsoft.com Restricted | link.springer.com Restricted | link.springer.com | CNR ExploRA


2021 Journal article Open Access OPEN

Conformity: a Path-Aware Homophily Measure for Node-Attributed Networks
Rossetti G., Citraro S., Milli L.
Unveiling 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 have 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. Different 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.Source: IEEE intelligent systems 36 (2021): 25–34. doi:10.1109/MIS.2021.3051291
DOI: 10.1109/mis.2021.3051291
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arXiv.org e-Print Archive Open Access | ieeexplore.ieee.org Open Access | IEEE Intelligent Systems Open Access | CNR ExploRA Open Access | IEEE Intelligent Systems Restricted | IEEE Intelligent Systems Restricted | IEEE Intelligent Systems Restricted | IEEE Intelligent Systems Restricted


2020 Conference article Open Access OPEN

Community-Aware Content Diffusion: Embeddednes and Permeability
Milli L., Rossetti G.
Viruses, opinions, ideas are different contents sharing a common trait: they need carriers embedded into a social context to spread. Modeling and approximating diffusive phenomena have always played an essential role in a varied range of applications from outbreak prevention to the analysis of meme and fake news. Classical approaches to such a task assume diffusion processes unfolding in a mean-field context, every actor being able to interact with all its peers. However, during the last decade, such an assumption has been progressively superseded by the availability of data modeling the real social network of individuals, thus producing a more reliable proxy for social interactions as spreading vehicles. In this work, following such a trend, we propose alternative ways of leveraging apriori knowledge on mesoscale network topology to design community-aware diffusion models with the aim of better approximate the spreading of content over complex and clustered social tissues.Source: International Conference on Complex Networks and their Applications, pp. 362–371, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_30
Project(s): SoBigData via OpenAIRE

See at: link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | rd.springer.com Restricted


2020 Report Restricted

UTLDR: an agent-based framework for modeling infectious diseases and public interventions
Rossetti G., Milli L., Citraro S., Morini V.
Nowadays, due to the SARS-CoV-2 pandemic, epidemic modelling is experiencing a constantly growing interest from researchers of heterogeneous fields of study. Indeed, the vast literature on computational epidemiology offers solid grounds for analytical studies and the definition of novel models aimed at both predictive and prescriptive scenario descriptions. To ease the access to diffusion modelling, several programming libraries and tools have been proposed during the last decade: however, to the best of our knowledge, none of them is explicitly designed to allow its users to integrate public interventions in their model. In this work, we introduce UTLDR, a framework that can simulate the effects of several public interventions (and their combinations) on the unfolding of epidemic processes. UTLDR enables the design of compartmental models incrementally and to simulate them over complex interaction network topologies. Moreover, it allows integrating external information on the analyzed population (e.g., age, gender, geographical allocation, and mobility patterns. . . ) and to use it to stratify and refine the designed model. After introducing the framework, we provide a few case studies to underline its flexibility and expressive power.Source: ISTI Working Papers, 2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: CNR ExploRA Restricted


2020 Report Open Access OPEN

Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks
Rossetti G., Citraro S., Milli L.
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.Source: ISTI Working Papers, 2020, 2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Contribution to conference Open Access OPEN

CDlib: A python library to extract, compare and evaluate communities from complex networks
Rossetti G., Milli L., Cazabet R.
In the last decades, the analysis of complex networks has received increasing attention from several, heterogeneous fields of research. One of the hottest topics in network science is Community Discovery (henceforth CD), the task of clustering network entities belonging to topological dense regions of a graph. Although many methods and algorithms have been proposed to cope with this problem, and related issues such as their evaluation and comparison, few of them are integrated into a common software framework, making hard and time-consuming to use, study and compare them. Only a handful of the most famous methods are available in generic libraries such as NetworkX and Igraph. To cope with this issue, we introduce a novel library designed to easily select/apply community discovery methods on network datasets, evaluate/compare the obtained clustering and visualize the results.Source: The 11th Conference on Network Modeling and Analysis, October 14 - 15, 2020

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2019 Journal article Open Access OPEN

CDLIB: a python library to extract, compare and evaluate communities from complex networks
Rossetti G., Milli L., Cazabet R.
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.Source: Applied network science 4 (2019). doi:10.1007/s41109-019-0165-9
DOI: 10.1007/s41109-019-0165-9
Project(s): SoBigData via OpenAIRE

See at: Applied Network Science Open Access | Applied Network Science Open Access | appliednetsci.springeropen.com Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2018 Journal article Open Access OPEN

NDlib: a python library to model and analyze diffusion processes over complex networks
Rossetti G., Milli L., Rinzivillo S., Sirbu A., Giannotti F., Pedreschi D.
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.Source: International Journal of Data Science and Analytics (Online) 5 (2018): 61–79. doi:10.1007/s41060-017-0086-6
DOI: 10.1007/s41060-017-0086-6
Project(s): CIMPLEX via OpenAIRE, SoBigData via OpenAIRE

See at: International Journal of Data Science and Analytics Open Access | ISTI Repository Open Access | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | CNR ExploRA Restricted


2018 Conference article Open Access OPEN

Diffusive Phenomena in Dynamic Networks: a data-driven study
Milli L., Rossetti G., Pedreschi D., Giannotti F.
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 been considered static, composed by a fixed set of nodes and edges. Recent studies describe social networks as rapidly changing topologies. In this work -- following a data-driven approach -- 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.Source: 9th Conference on Complex Networks, CompleNet, pp. 151–159, Boston, USA, 6/3/2018
DOI: 10.1007/978-3-319-73198-8_13
Project(s): CIMPLEX via OpenAIRE, SoBigData via OpenAIRE

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted


2018 Journal article Open Access OPEN

Active and passive diffusion processes in complex networks
Milli L., Rossetti G., Pedreschi D., Giannotti F.
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.Source: Applied network science 3 (2018). doi:10.1007/s41109-018-0100-5
DOI: 10.1007/s41109-018-0100-5
Project(s): SoBigData via OpenAIRE

See at: Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Europe PubMed Central Open Access | Archivio della Ricerca - Università di Pisa Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Applied Network Science Open Access


2018 Contribution to conference Restricted

NDlib: A Python Library to Model and Analyze Diffusion Processes over Complex Networks
Rossetti G., Milli L., Rinzivillo S.
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 we introduced a novel framework, named 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. 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.Source: The Web Conference, pp. 183–186, 23-27 April 2018
DOI: 10.1145/3184558.3186974
Project(s): SoBigData via OpenAIRE

See at: academic.microsoft.com Restricted | arpi.unipi.it Restricted | arxiv.org Restricted | dblp.uni-trier.de Restricted | dl.acm.org Restricted | dl.acm.org Restricted | CNR ExploRA Restricted


2017 Conference article Restricted

NDlib: Studying network diffusion dynamics
Rossetti G., Milli L., Rinzivillo S., Sirbu A., Pedreschi D., Giannotti F.
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, NDlib. NDlib 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.Source: Data Science and Advanced Analytics (DSAA), pp. 155–164, Tokyo, Japan, 9/10/2017
DOI: 10.1109/dsaa.2017.6
Project(s): CIMPLEX via OpenAIRE, SoBigData via OpenAIRE

See at: academic.microsoft.com Restricted | core.ac.uk Restricted | dblp.uni-trier.de Restricted | Archivio della Ricerca - Università di Pisa Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | xplorestaging.ieee.org Restricted


2017 Journal article Open Access OPEN

Forecasting success via early adoptions analysis: a data-driven study
Rossetti G., Milli L., Giannotti F., Pedreschi D.
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'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.Source: PloS one 12 (2017). doi:10.1371/journal.pone.0189096
DOI: 10.1371/journal.pone.0189096
Project(s): CIMPLEX via OpenAIRE, SoBigData via OpenAIRE

See at: PLoS ONE Open Access | PLoS ONE Open Access | PLoS ONE Open Access | PLoS ONE Open Access | PLoS ONE Open Access | PLoS ONE Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | PLoS ONE Open Access | PLoS ONE Open Access | PLoS ONE Open Access


2017 Conference article Open Access OPEN

Information diffusion in complex networks: The active/passive conundrum
Milli L., Rossetti G., Pedreschi D., Giannotti F.
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.Source: Complex Networks 2017 - Sixth International Conference on Complex Networks and Their Applications, pp. 305–313, 01/10/2017
DOI: 10.1007/978-3-319-72150-7_25
Project(s): CIMPLEX via OpenAIRE, SoBigData via OpenAIRE

See at: www.springer.com Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted


2015 Conference article Open Access OPEN

Quantification in social networks
Milli L., Monreale A., Rossetti G., Pedreschi D., Giannotti F., Sebastiani F.
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.Source: IEEE International Conference on Data Science and Advanced Analytics, Paris, France, 19-21/10/2015
DOI: 10.1109/dsaa.2015.7344845
Project(s): CIMPLEX via OpenAIRE

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | arpi.unipi.it Restricted | dblp.uni-trier.de Restricted | ieeexplore.ieee.org Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | xplorestaging.ieee.org Restricted


2013 Conference article Restricted

Quantification trees
Milli L., Monreale A., Rossetti G., Giannotti F., Pedreschi D., Sebastiani F.
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 suboptimal, 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.Source: ICDM 2013 - 13th IEEE International Conference on Data Mining, pp. 528–536, Dallas, US, 7-12 December 2013
DOI: 10.1109/icdm.2013.122

See at: CNR ExploRA Restricted | xplorestaging.ieee.org Restricted