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
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
Conference article
Open Access
The fractal dimension of music: geography, popularity and sentiment analysis
Pollacci L., Rossetti G., Guidotti R., Giannotti F., Pedreschi D.
Nowadays there is a growing standardization of musical con- tents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a "fractal" musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians' popular- ity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.Source: GOODTECHS 2017 - Third International Conference on Smart Objects and Technologies for Social Good, pp. 183–194, Pisa, Italy, 29-30 November 2017
DOI: 10.1007/978-3-319-76111-4_19
Project(s): SoBigData
Metrics:
Pollacci L., Rossetti G., Guidotti R., Giannotti F., Pedreschi D.
Nowadays there is a growing standardization of musical con- tents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a "fractal" musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians' popular- ity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.Source: GOODTECHS 2017 - Third International Conference on Smart Objects and Technologies for Social Good, pp. 183–194, Pisa, Italy, 29-30 November 2017
DOI: 10.1007/978-3-319-76111-4_19
Project(s): SoBigData
Metrics:
See at:
ISTI Repository | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering | link.springer.com | CNR ExploRA
2018
Contribution to conference
Open Access
OSNED 2018 Chairs' Welcome & Organization
Cazabet R., Passarella A., Rossetti G., Silvestri F.
It is our great pleasure to welcome you to the WWW 2018 OSNED workshop (Online Social Networks and Media: Network Properties and Dynamics). Online Social Networks and Media (OSNEM) are one of the most disruptive communication platforms of the last 15 years with high socio-economic value. Within this framework, the network properties of OSNEM can be used to capture multiple phenomena related to OSNEM, at different logical layers, from a technical perspective (e.g., OSNEM data management and information diffusion), as well as a societal perspective (e.g., the OSNEM users' social structures). Moreover, the analysis of network dynamics represents one of the biggest challenges that emerged in recent years within the network science community.
Cazabet R., Passarella A., Rossetti G., Silvestri F.
It is our great pleasure to welcome you to the WWW 2018 OSNED workshop (Online Social Networks and Media: Network Properties and Dynamics). Online Social Networks and Media (OSNEM) are one of the most disruptive communication platforms of the last 15 years with high socio-economic value. Within this framework, the network properties of OSNEM can be used to capture multiple phenomena related to OSNEM, at different logical layers, from a technical perspective (e.g., OSNEM data management and information diffusion), as well as a societal perspective (e.g., the OSNEM users' social structures). Moreover, the analysis of network dynamics represents one of the biggest challenges that emerged in recent years within the network science community.
See at:
ISTI Repository | osned.netsons.org | 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
2018
Contribution to book
Open Access
How data mining and machine learning evolved from relational data base to data science
Amato G., Candela L., Castelli D., Esuli A., Falchi F., Gennaro C., Giannotti F., Monreale A., Nanni M., Pagano P., Pappalardo L., Pedreschi D., Pratesi F., Rabitti F., Rinzivillo S., Rossetti G., Ruggieri S., Sebastiani F., Tesconi M.
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, edited by Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Saccà, pp. 287–306, 2018
DOI: 10.1007/978-3-319-61893-7_17
Metrics:
Amato G., Candela L., Castelli D., Esuli A., Falchi F., Gennaro C., Giannotti F., Monreale A., Nanni M., Pagano P., Pappalardo L., Pedreschi D., Pratesi F., Rabitti F., Rinzivillo S., Rossetti G., Ruggieri S., Sebastiani F., Tesconi M.
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, edited by Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Saccà, pp. 287–306, 2018
DOI: 10.1007/978-3-319-61893-7_17
Metrics:
See at:
arpi.unipi.it | ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2018
Conference article
Open Access
Discovering Mobility Functional Areas: A Mobility Data Analysis Approach
Gabrielli L., Fadda D., Rossetti G., Nanni M., Piccinini L., Pedreschi D., Giannotti F., Lattarulo P.
How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained.Source: 9th Conference on Complex Networks, CompleNet, pp. 311–322, Boston, 6/03/2018
DOI: 10.1007/978-3-319-73198-8_27
Project(s): SoBigData
Metrics:
Gabrielli L., Fadda D., Rossetti G., Nanni M., Piccinini L., Pedreschi D., Giannotti F., Lattarulo P.
How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained.Source: 9th Conference on Complex Networks, CompleNet, pp. 311–322, Boston, 6/03/2018
DOI: 10.1007/978-3-319-73198-8_27
Project(s): SoBigData
Metrics:
See at:
ISTI Repository | ISTI Repository | Springer Proceedings in Complexity | link.springer.com | CNR ExploRA
2018
Journal article
Open Access
Community discovery in dynamic networks: A survey
Rossetti G., Cazabet R.
Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a "user manual," this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.Source: ACM computing surveys 51 (2018). doi:10.1145/3172867
DOI: 10.1145/3172867
DOI: 10.48550/arxiv.1707.03186
Metrics:
Rossetti G., Cazabet R.
Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a "user manual," this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.Source: ACM computing surveys 51 (2018). doi:10.1145/3172867
DOI: 10.1145/3172867
DOI: 10.48550/arxiv.1707.03186
Metrics:
See at:
arXiv.org e-Print Archive | ACM Computing Surveys | ISTI Repository | dl.acm.org | ACM Computing Surveys | doi.org | HAL-ENS-LYON | 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