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2021
Journal article
Open Access
Opinion dynamic modeling of news perception
Milli L.
During the last decade, the advent of the Web and online social networks rapidly changed the way we were used to search, gather and discuss information of any kind. These tools have given everyone the chance to become a news medium. While promoting more democratic access to information, direct and unfiltered communication channels may increase our chances to confront malicious/misleading behavior. Fake news diffusion represents one of the most pressing issues of our online society. In recent years, fake news has been analyzed from several perspectives; among such vast literature, an important theme is the analysis of fake news' perception. In this work, moving from such observation, I propose a family of opinion dynamics models to understand the role of specific social factors on the acceptance/rejection of news contents. In particular, I model and discuss the effect that stubborn agents, different levels of trust among individuals, open-mindedness, attraction/repulsion phenomena, and similarity between agents have on the population dynamics of news perception. To discuss the peculiarities of the proposed models, I tested them on two synthetic network topologies thus underlying when/how they affect the stable states reached by the performed simulations.Source: Applied network science 6 (2021): 1–19. doi:10.1007/s41109-021-00412-4
DOI: 10.1007/s41109-021-00412-4
Project(s): SoBigData-PlusPlus
Metrics:
Milli L.
During the last decade, the advent of the Web and online social networks rapidly changed the way we were used to search, gather and discuss information of any kind. These tools have given everyone the chance to become a news medium. While promoting more democratic access to information, direct and unfiltered communication channels may increase our chances to confront malicious/misleading behavior. Fake news diffusion represents one of the most pressing issues of our online society. In recent years, fake news has been analyzed from several perspectives; among such vast literature, an important theme is the analysis of fake news' perception. In this work, moving from such observation, I propose a family of opinion dynamics models to understand the role of specific social factors on the acceptance/rejection of news contents. In particular, I model and discuss the effect that stubborn agents, different levels of trust among individuals, open-mindedness, attraction/repulsion phenomena, and similarity between agents have on the population dynamics of news perception. To discuss the peculiarities of the proposed models, I tested them on two synthetic network topologies thus underlying when/how they affect the stable states reached by the performed simulations.Source: Applied network science 6 (2021): 1–19. doi:10.1007/s41109-021-00412-4
DOI: 10.1007/s41109-021-00412-4
Project(s): SoBigData-PlusPlus
Metrics:
See at:
appliednetsci.springeropen.com 
| ISTI Repository | CNR ExploRA
2019
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
appliednetsci.springeropen.com | Applied Network Science | Applied Network Science | Applied Network Science | ISTI Repository | HAL-ENS-LYON 
| www.scopus.com | CNR ExploRA
2020
Conference article
Open Access
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, Lisbon, Portugal, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_30
Project(s): SoBigData
Metrics:
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, Lisbon, Portugal, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_30
Project(s): SoBigData
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2020
Contribution to conference
Open Access
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
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 | ISTI Repository | CNR ExploRA
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
Metrics:
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
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2022
Journal article
Open Access
Modeling algorithmic bias: simplicial complexes and evolving network topologies
Pansanella V., Rossetti G., Milli L.
Every day, people inform themselves and create their opinions on social networks. Although these platforms have promoted the access and dissemination of information, they may expose readers to manipulative, biased, and disinformative content--co-causes of polarization/radicalization. Moreover, recommendation algorithms, intended initially to enhance platform usage, are likely to augment such phenomena, generating the so-called Algorithmic Bias. In this work, we propose two extensions of the Algorithmic Bias model and analyze them on scale-free and Erd?s-Rényi random network topologies. Our first extension introduces a mechanism of link rewiring so that the underlying structure co-evolves with the opinion dynamics, generating the Adaptive Algorithmic Bias model. The second one explicitly models a peer-pressure mechanism where a majority--if there is one--can attract a disagreeing individual, pushing them to conform. As a result, we observe that the co-evolution of opinions and network structure does not significantly impact the final state when the latter is much slower than the former. On the other hand, peer pressure enhances consensus mitigating the effects of both "close-mindedness" and algorithmic filtering.Source: Applied network science 7 (2022). doi:10.1007/s41109-022-00495-7
DOI: 10.1007/s41109-022-00495-7
Metrics:
Pansanella V., Rossetti G., Milli L.
Every day, people inform themselves and create their opinions on social networks. Although these platforms have promoted the access and dissemination of information, they may expose readers to manipulative, biased, and disinformative content--co-causes of polarization/radicalization. Moreover, recommendation algorithms, intended initially to enhance platform usage, are likely to augment such phenomena, generating the so-called Algorithmic Bias. In this work, we propose two extensions of the Algorithmic Bias model and analyze them on scale-free and Erd?s-Rényi random network topologies. Our first extension introduces a mechanism of link rewiring so that the underlying structure co-evolves with the opinion dynamics, generating the Adaptive Algorithmic Bias model. The second one explicitly models a peer-pressure mechanism where a majority--if there is one--can attract a disagreeing individual, pushing them to conform. As a result, we observe that the co-evolution of opinions and network structure does not significantly impact the final state when the latter is much slower than the former. On the other hand, peer pressure enhances consensus mitigating the effects of both "close-mindedness" and algorithmic filtering.Source: Applied network science 7 (2022). doi:10.1007/s41109-022-00495-7
DOI: 10.1007/s41109-022-00495-7
Metrics:
See at:
appliednetsci.springeropen.com | ISTI Repository | CNR ExploRA
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
Metrics:
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
Metrics:
See at:
xplorestaging.ieee.org | CNR ExploRA
2017
Journal article
Open Access
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, SoBigData
Metrics:
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
, SoBigData Metrics:
See at:
PLoS ONE | PLoS ONE | PLoS ONE | PLoS ONE | journals.plos.org | ISTI Repository | CNR ExploRA
2017
Conference article
Open Access
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, SoBigData
Metrics:
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
, SoBigData Metrics:
See at:
media.springer.com | Studies in Computational Intelligence | link.springer.com | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR ExploRA
2018
Conference article
Open Access
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, SoBigData
Metrics:
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
, SoBigData Metrics:
See at:
ISTI Repository | Springer Proceedings in Complexity | link.springer.com | CNR ExploRA
2018
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
Applied Network Science | Applied Network Science | Applied Network Science | Archivio della Ricerca - Università di Pisa | Applied Network Science | ISTI Repository | CNR ExploRA
2020
Report
Unknown
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
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
See at: CNR ExploRA
2020
Report
Open Access
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
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
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2018
Contribution to conference
Open Access
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
Metrics:
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
Metrics:
See at:
dl.acm.org | dl.acm.org | doi.org | CNR ExploRA
2021
Journal article
Open Access
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
DOI: 10.48550/arxiv.2012.05195
Project(s): SoBigData-PlusPlus
Metrics:
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
DOI: 10.48550/arxiv.2012.05195
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | IEEE Intelligent Systems | ieeexplore.ieee.org | IEEE Intelligent Systems | ISTI Repository | doi.org | CNR ExploRA
2022
Conference article
Open Access
From mean-field to complex topologies: network effects on the algorithmic bias model
Pansanella V., Rossetti G., Milli L.
Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarization/radicalization. Moreover, recommendation algorithms - intended to enhance platform usage - are likely to augment such phenomena, generating the so called Algorithmic Bias. In this work, we study the impact that different network topologies have on the formation and evolution of opinion in the context of a recent opinion dynamic model which includes bounded confidence and algorithmic bias. Mean-field, scale-free and random topologies, as well as networks generated by the Lancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinion fragmentation/polarization and time to convergence.Source: COMPLEX NETWORKS 2021 - Tenth International Conference on Complex Networks and Their Applications, pp. 329–340, Madrid, Spain, 30/11-2/12/2021
DOI: 10.1007/978-3-030-93413-2_28
Project(s): SoBigData-PlusPlus
Metrics:
Pansanella V., Rossetti G., Milli L.
Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarization/radicalization. Moreover, recommendation algorithms - intended to enhance platform usage - are likely to augment such phenomena, generating the so called Algorithmic Bias. In this work, we study the impact that different network topologies have on the formation and evolution of opinion in the context of a recent opinion dynamic model which includes bounded confidence and algorithmic bias. Mean-field, scale-free and random topologies, as well as networks generated by the Lancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinion fragmentation/polarization and time to convergence.Source: COMPLEX NETWORKS 2021 - Tenth International Conference on Complex Networks and Their Applications, pp. 329–340, Madrid, Spain, 30/11-2/12/2021
DOI: 10.1007/978-3-030-93413-2_28
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2022
Journal article
Open Access
Delta-Conformity: multi-scale node assortativity in feature-rich stream graphs
Citraro S., Milli L., Cazabet R., Rossetti G.
Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the timevarying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: Delta-Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes' connectivity along a period of time Delta starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce Delta-Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.Source: International Journal of Data Science and Analytics (Print) (2022). doi:10.1007/s41060-077-00175-4
DOI: 10.1007/s41060-077-00175-4
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S., Milli L., Cazabet R., Rossetti G.
Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the timevarying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: Delta-Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes' connectivity along a period of time Delta starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce Delta-Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.Source: International Journal of Data Science and Analytics (Print) (2022). doi:10.1007/s41060-077-00175-4
DOI: 10.1007/s41060-077-00175-4
Project(s): SoBigData-PlusPlus
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Academic mobility from a big data perspective
Pollacci L., Milli L., Bircan T., Rossetti G.
Understanding the careers and movements of highly skilled people plays an ever-increasing role in today's global knowledgebased economy. Researchers and academics are sources of innovation and development for governments and institutions. Our study uses scientific-related data to track careers evolution and Researchers' movements over time. To this end, we define the Yearly Degree of Collaborations Index, which measures the annual tendency of researchers to collaborate intra-nationally, and two scores to measure the mobility in and out of countries, as well as their balance.Source: International Journal of Data Science and Analytics (Online) (2023). doi:10.1007/s41060-023-00432-6
DOI: 10.1007/s41060-023-00432-6
DOI: 10.21203/rs.3.rs-1510153/v1
Project(s): HumMingBird, SoBigData-PlusPlus
Metrics:
Pollacci L., Milli L., Bircan T., Rossetti G.
Understanding the careers and movements of highly skilled people plays an ever-increasing role in today's global knowledgebased economy. Researchers and academics are sources of innovation and development for governments and institutions. Our study uses scientific-related data to track careers evolution and Researchers' movements over time. To this end, we define the Yearly Degree of Collaborations Index, which measures the annual tendency of researchers to collaborate intra-nationally, and two scores to measure the mobility in and out of countries, as well as their balance.Source: International Journal of Data Science and Analytics (Online) (2023). doi:10.1007/s41060-023-00432-6
DOI: 10.1007/s41060-023-00432-6
DOI: 10.21203/rs.3.rs-1510153/v1
Project(s): HumMingBird
, SoBigData-PlusPlus Metrics:
See at:
International Journal of Data Science and Analytics | doi.org | link.springer.com | ISTI Repository | CNR ExploRA
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, SoBigData
Metrics:
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
, SoBigData Metrics:
See at:
doi.org | ieeexplore.ieee.org | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR ExploRA
2018
Journal article
Open Access
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
DOI: 10.48550/arxiv.1801.05854
Project(s): CIMPLEX, SoBigData
Metrics:
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
DOI: 10.48550/arxiv.1801.05854
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
arXiv.org e-Print Archive | International Journal of Data Science and Analytics | Archivio della Ricerca - Università di Pisa | ISTI Repository | International Journal of Data Science and Analytics | doi.org | CNR ExploRA