2019
Contribution to journal
Restricted
Editorial - Special issue on OSNEM network properties and dynamics
Cazabet R., Passarella A., Rossetti G., Silvestri F.
Source: Online social networks and media 12 (2019): 21–21. doi:10.1016/j.osnem.2019.06.001
DOI: 10.1016/j.osnem.2019.06.001
Metrics:
Cazabet R., Passarella A., Rossetti G., Silvestri F.
Source: Online social networks and media 12 (2019): 21–21. doi:10.1016/j.osnem.2019.06.001
DOI: 10.1016/j.osnem.2019.06.001
Metrics:
See at:
Online Social Networks and Media 
| www.sciencedirect.com | CNR ExploRA
2017
Contribution to book
Open Access
Dynamic Community Detection
Cazabet R., Rossetti G., Amblard F.
Community detection is one of the most popular topics in the field of network analysis. Since the seminal paper of Girvan and Newman (2002), hundreds of papers have been published on the topic. From the initial problem of graph partitioning, in which each node of the network must belong to one and only one community, new aspects of community structures have been taken into consideration, such as overlapping communities and hierarchical decomposition. Recently, new methods have been proposed, which can handle dynamic networks. The communities found by these algorithms are called dynamic communities.DOI: 10.1007/978-1-4614-7163-9_383-1
DOI: 10.1007/978-1-4939-7131-2_383
Metrics:
Cazabet R., Rossetti G., Amblard F.
Community detection is one of the most popular topics in the field of network analysis. Since the seminal paper of Girvan and Newman (2002), hundreds of papers have been published on the topic. From the initial problem of graph partitioning, in which each node of the network must belong to one and only one community, new aspects of community structures have been taken into consideration, such as overlapping communities and hierarchical decomposition. Recently, new methods have been proposed, which can handle dynamic networks. The communities found by these algorithms are called dynamic communities.DOI: 10.1007/978-1-4614-7163-9_383-1
DOI: 10.1007/978-1-4939-7131-2_383
Metrics:
See at:
hal.archives-ouvertes.fr 
| doi.org | doi.org | Hyper Article en Ligne | CNR ExploRA
2013
Conference article
Unknown
On multidimensional network measures
Magnani M., Monreale A., Rossetti G., Giannotti F.
Networks, i.e., sets of interconnected entities, are ubiquitous, spanning disciplines as diverse as sociology, biology and computer sci- ence. The recent availability of large amounts of network data has thus provided a unique opportunity to develop models and analysis tools ap- plicable to a wide range of scenarios. However, real-world phenomena are often more complex than existing graph data models. One relevant ex- ample concerns the numerous types of social relationships (or edges) that can be present between individuals in a social network. In this short pa- per we present a uni ed model and a set of measures recently developed to represent and analyze network data with multiple types of edges.Source: SEDB 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 215–222, Roccella Jonica, Reggio Calabria, Italy, 30 June - 3 July 2013
Magnani M., Monreale A., Rossetti G., Giannotti F.
Networks, i.e., sets of interconnected entities, are ubiquitous, spanning disciplines as diverse as sociology, biology and computer sci- ence. The recent availability of large amounts of network data has thus provided a unique opportunity to develop models and analysis tools ap- plicable to a wide range of scenarios. However, real-world phenomena are often more complex than existing graph data models. One relevant ex- ample concerns the numerous types of social relationships (or edges) that can be present between individuals in a social network. In this short pa- per we present a uni ed model and a set of measures recently developed to represent and analyze network data with multiple types of edges.Source: SEDB 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 215–222, Roccella Jonica, Reggio Calabria, Italy, 30 June - 3 July 2013
See at: CNR ExploRA
2015
Report
Open Access
ISTI young research award 2015
Bardi A., Candela L., Coro G., Dellepiane M., Esuli A., Gabrielli L., Gotta A., Lucchese C., Marcheggiani D., Nardini F. M., Palumbo F., Pietroni N., Rossetti G.
The ISTI Young Researcher Award is an award for young people of Institute of Information Science and Technologies (ISTI) with high scientific production. In particular, the award is granted to young staff members (less than 35 years old) by assessing the yearly scientific production of the year preceding the award. This report documents procedure and results of the 2015 edition of the award.Source: ISTI Technical reports, 2015
Bardi A., Candela L., Coro G., Dellepiane M., Esuli A., Gabrielli L., Gotta A., Lucchese C., Marcheggiani D., Nardini F. M., Palumbo F., Pietroni N., Rossetti G.
The ISTI Young Researcher Award is an award for young people of Institute of Information Science and Technologies (ISTI) with high scientific production. In particular, the award is granted to young staff members (less than 35 years old) by assessing the yearly scientific production of the year preceding the award. This report documents procedure and results of the 2015 edition of the award.Source: ISTI Technical reports, 2015
See at:
ISTI Repository | CNR ExploRA
2016
Conference article
Open Access
"Are we playing like Music-Stars?" Placing emerging artists on the Italian music scene
Pollacci L., Guidotti R., Rossetti G.
The Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a study of the Italian music scene and how the new generation of musicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional music and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results obtained are a first attempt to outline rules suggesting the importance of those features needed to increase popularity in the Italian music scene.Source: 9th International Workshop on Machine Learning and Music, pp. 51–55, Riva del Garda, Italy, 23 September 2016
Pollacci L., Guidotti R., Rossetti G.
The Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a study of the Italian music scene and how the new generation of musicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional music and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results obtained are a first attempt to outline rules suggesting the importance of those features needed to increase popularity in the Italian music scene.Source: 9th International Workshop on Machine Learning and Music, pp. 51–55, Riva del Garda, Italy, 23 September 2016
See at:
sites.google.com | CNR ExploRA
2016
Conference article
Restricted
Football market strategies: think locally, trade globally
Rossetti G., Caproni V.
Every year football clubs trade players in order to build competitive rosters able to compete for success, increase the number of their supporters and amplify sponsors and media attention. In the complex system described by the football transfer market can we identify the strategies pursued by successful teams? Where do they search for new talents? Does it pay to constantly change the club roster? In this work we identify archetypal market strategies over 25 years of transfer market as depicted by UEFA professional clubs and study their impact on sportive success. Our analysis underline how, regardless from clubs' available budgets, transfer market strategies deeply impact - on the long run - football sportive performancesSource: ICDMW 2016 - IEEE 16th International Conference on Data Mining Workshops, pp. 152–159, Barcellona, Spain, 12-15 December 2016
DOI: 10.1109/icdmw.2016.0029
Project(s): SoBigData
Metrics:
Rossetti G., Caproni V.
Every year football clubs trade players in order to build competitive rosters able to compete for success, increase the number of their supporters and amplify sponsors and media attention. In the complex system described by the football transfer market can we identify the strategies pursued by successful teams? Where do they search for new talents? Does it pay to constantly change the club roster? In this work we identify archetypal market strategies over 25 years of transfer market as depicted by UEFA professional clubs and study their impact on sportive success. Our analysis underline how, regardless from clubs' available budgets, transfer market strategies deeply impact - on the long run - football sportive performancesSource: ICDMW 2016 - IEEE 16th International Conference on Data Mining Workshops, pp. 152–159, Barcellona, Spain, 12-15 December 2016
DOI: 10.1109/icdmw.2016.0029
Project(s): SoBigData
Metrics:
See at:
doi.org | ieeexplore.ieee.org | 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
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
2017
Journal article
Open Access
RDyn: graph benchmark handling community dynamics
Rossetti G.
Graph models provide an understanding of the dynamics of network formation and evolution; as a direct consequence, synthesizing graphs having controlled topology and planted partitions has been often identified as a strategy to describe benchmarks able to assess the performances of community discovery algorithm. However, one relevant aspect of real-world networks has been ignored by benchmarks proposed so far: community dynamics. As time goes by network communities rise, fall and may interact with each other generating merges and splits. Indeed, during the last decade dynamic community discovery has become a very active research field: in order to provide a coherent environment to test novel algorithms aimed at identifying mutable network partitions we introduce RDYN, an approach able to generates dynamic networks along with time-dependent ground-truth partitions having tunable quality.Source: Journal of complex networks (Online) 5 (2017): 893–912. doi:10.1093/comnet/cnx016
DOI: 10.1093/comnet/cnx016
Project(s): CIMPLEX, SoBigData
Metrics:
Rossetti G.
Graph models provide an understanding of the dynamics of network formation and evolution; as a direct consequence, synthesizing graphs having controlled topology and planted partitions has been often identified as a strategy to describe benchmarks able to assess the performances of community discovery algorithm. However, one relevant aspect of real-world networks has been ignored by benchmarks proposed so far: community dynamics. As time goes by network communities rise, fall and may interact with each other generating merges and splits. Indeed, during the last decade dynamic community discovery has become a very active research field: in order to provide a coherent environment to test novel algorithms aimed at identifying mutable network partitions we introduce RDYN, an approach able to generates dynamic networks along with time-dependent ground-truth partitions having tunable quality.Source: Journal of complex networks (Online) 5 (2017): 893–912. doi:10.1093/comnet/cnx016
DOI: 10.1093/comnet/cnx016
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
ISTI Repository | academic.oup.com | Journal of Complex Networks | 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
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
Eva: attribute-aware network segmentation
Citraro S., Rossetti G.
Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.Source: International Conference on Complex Networks and their Applications, pp. 141–151, Lisbon, Portugal, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_12
DOI: 10.48550/arxiv.1910.06599
Project(s): SoBigData
Metrics:
Citraro S., Rossetti G.
Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.Source: International Conference on Complex Networks and their Applications, pp. 141–151, Lisbon, Portugal, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_12
DOI: 10.48550/arxiv.1910.06599
Project(s): SoBigData
Metrics:
See at:
arXiv.org e-Print Archive | arxiv.org | ISTI Repository | doi.org | doi.org | link.springer.com | 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
2011
Conference article
Unknown
Link Prediction su Reti Multidimensionali
Rossetti G., Berlingerio M., Giannotti F.
L' analisi di reti complesse e un campo di ricerca interdisciplinare, che vede coinvolti fisici, sociologi, matematici, economisti e informatici. In questo articolo estendiamo la formulazione classica del problema del Link Prediction allo scenario delle reti multidimensionali, ossia quelle reti che ammettono pìu di un link fra due entità. Introduciamo una nuova formulazione che tenga conto delle informazioni multidimensionali espresse dalle reti analizzate, e alcune famiglie di predittori progettati appositamente per sfruttare tali informazioni. Presentiamo infine una valutazione sperimentale dell applicazione delle soluzioni proposte a reti multidimensionali reali. I risultati preliminari ottenuti sono incoraggianti, e spingono verso una ricerca pìu estensiva di soluzioni al problema del Link Prediction su reti multidimensionali.Source: Proceedings of the 19th Italian Symposium on Advanced Database Systems, pp. 350–357, June 26-29 2011
Rossetti G., Berlingerio M., Giannotti F.
L' analisi di reti complesse e un campo di ricerca interdisciplinare, che vede coinvolti fisici, sociologi, matematici, economisti e informatici. In questo articolo estendiamo la formulazione classica del problema del Link Prediction allo scenario delle reti multidimensionali, ossia quelle reti che ammettono pìu di un link fra due entità. Introduciamo una nuova formulazione che tenga conto delle informazioni multidimensionali espresse dalle reti analizzate, e alcune famiglie di predittori progettati appositamente per sfruttare tali informazioni. Presentiamo infine una valutazione sperimentale dell applicazione delle soluzioni proposte a reti multidimensionali reali. I risultati preliminari ottenuti sono incoraggianti, e spingono verso una ricerca pìu estensiva di soluzioni al problema del Link Prediction su reti multidimensionali.Source: Proceedings of the 19th Italian Symposium on Advanced Database Systems, pp. 350–357, June 26-29 2011
See at: dblp.org | CNR ExploRA
2021
Contribution to conference
Open Access
Sockpuppet detection: a Telegram case study
Pisciotta G., Somenzi M., Barisani E., Rossetti G.
In Online Social Networks (OSN) numerous are the cases in which users create multiple accounts that publicly seem to belong to different people but are actually fake identities of the same person. These fictitious characters can be exploited to carry out abusive behaviors such as manipulating opinions, spreading fake news and disturbing other users. In literature this problem is known as the Sockpuppet problem. In our work we focus on Telegram, a wide-spread instant messaging application, often known for its exploitation by members of organized crime and terrorism, and more in general for its high presence of people who have offensive behaviors.Source: Complex Networks 2020 - The 9th International Conference on Complex Networks and their Applications, Online conference, 1-3/12/2020
Project(s): SoBigData-PlusPlus
Pisciotta G., Somenzi M., Barisani E., Rossetti G.
In Online Social Networks (OSN) numerous are the cases in which users create multiple accounts that publicly seem to belong to different people but are actually fake identities of the same person. These fictitious characters can be exploited to carry out abusive behaviors such as manipulating opinions, spreading fake news and disturbing other users. In literature this problem is known as the Sockpuppet problem. In our work we focus on Telegram, a wide-spread instant messaging application, often known for its exploitation by members of organized crime and terrorism, and more in general for its high presence of people who have offensive behaviors.Source: Complex Networks 2020 - The 9th International Conference on Complex Networks and their Applications, Online conference, 1-3/12/2020
Project(s): SoBigData-PlusPlus
See at:
arxiv.org | ISTI Repository | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
X-Mark: a benchmark for node-attributed community discovery algorithms
Citraro S., Rossetti G.
Grouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.Source: Social Network Analysis and Mining 11 (2021). doi:10.1007/s13278-021-00823-2
DOI: 10.1007/s13278-021-00823-2
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S., Rossetti G.
Grouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.Source: Social Network Analysis and Mining 11 (2021). doi:10.1007/s13278-021-00823-2
DOI: 10.1007/s13278-021-00823-2
Project(s): SoBigData-PlusPlus
Metrics:
See at:
link.springer.com | Social Network Analysis and Mining | Social Network Analysis and Mining | 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:
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International Journal of Data Science and Analytics | doi.org | link.springer.com | ISTI Repository | CNR ExploRA
2011
Conference article
Restricted
Scalable Link Prediction on Multidimensional Networks
Rossetti Giulio, Berlingerio Michele, Giannotti Fosca
Complex networks have been receiving increasing attention by the scientific community, also due to the availability of massive network data from diverse domains. One problem largely studied so far is Link Prediction, i.e. the problem of predicting new upcoming connections in the network. However, one aspect of complex networks has been disregarded so far: real networks are often multidimensional, i.e. multiple connections may reside between any two nodes. In this context, we define the problem of Multidimensional Link Prediction, and we introduce several predictors based on structural analysis of the networks. We present the results obtained on real networks, showing the performances of both the introduced multidimensional versions of the Common Neighbors and Adamic-Adar, and the derived predictors aimed at capturing the multidimensional and temporal information extracted from the data. Our findings show that the evolution of multidimensional networks can be predicted, and that supervised models may improve the accuracy of underlying unsupervised predictors, if used in conjunction with them.Source: The IEEE International Conference on Data Mining series, Workshop 2011, ICDMW, pp. 979–986, Vancouver - Canada, 11-14 /12 2011
DOI: 10.1109/icdmw.2011.150
Metrics:
Rossetti Giulio, Berlingerio Michele, Giannotti Fosca
Complex networks have been receiving increasing attention by the scientific community, also due to the availability of massive network data from diverse domains. One problem largely studied so far is Link Prediction, i.e. the problem of predicting new upcoming connections in the network. However, one aspect of complex networks has been disregarded so far: real networks are often multidimensional, i.e. multiple connections may reside between any two nodes. In this context, we define the problem of Multidimensional Link Prediction, and we introduce several predictors based on structural analysis of the networks. We present the results obtained on real networks, showing the performances of both the introduced multidimensional versions of the Common Neighbors and Adamic-Adar, and the derived predictors aimed at capturing the multidimensional and temporal information extracted from the data. Our findings show that the evolution of multidimensional networks can be predicted, and that supervised models may improve the accuracy of underlying unsupervised predictors, if used in conjunction with them.Source: The IEEE International Conference on Data Mining series, Workshop 2011, ICDMW, pp. 979–986, Vancouver - Canada, 11-14 /12 2011
DOI: 10.1109/icdmw.2011.150
Metrics:
See at:
doi.org | CNR ExploRA