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
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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
2016
Contribution to book
Restricted
Audio ergo sum. A personal data model for musical preferences
Guidotti R., Rossetti G., Pedreschi D.
Nobody can state " Rock is my favorite genre " or " David Bowie is my favorite artist ". We defined a Personal Listening Data Model able to capture musical preferences through indicators and patterns, and we discovered that we are all characterized by a limited set of musical preferences, but not by a unique predilection. The empowered capacity of mobile devices and their growing adoption in our everyday life is generating an enormous increment in the production of personal data such as calls, positioning, online purchases and even music listening. Musical listening is a type of data that has started receiving more attention from the scientific community as consequence of the increasing availability of rich and punctual online data sources. Starting from the listening of 30k Last.Fm users, we show how the employment of the Personal Listening Data Models can provide higher levels of self-awareness. In addition, the proposed model will enable the development of a wide range of analysis and musical services both at personal and at collective level.Source: Software Technologies: Applications and Foundations, edited by Milazzo, Paolo; Varró, Dániel; Wimmer, Manuel, pp. 51–66, 2016
DOI: 10.1007/978-3-319-50230-4_5
Project(s): CIMPLEX
Metrics:
Guidotti R., Rossetti G., Pedreschi D.
Nobody can state " Rock is my favorite genre " or " David Bowie is my favorite artist ". We defined a Personal Listening Data Model able to capture musical preferences through indicators and patterns, and we discovered that we are all characterized by a limited set of musical preferences, but not by a unique predilection. The empowered capacity of mobile devices and their growing adoption in our everyday life is generating an enormous increment in the production of personal data such as calls, positioning, online purchases and even music listening. Musical listening is a type of data that has started receiving more attention from the scientific community as consequence of the increasing availability of rich and punctual online data sources. Starting from the listening of 30k Last.Fm users, we show how the employment of the Personal Listening Data Models can provide higher levels of self-awareness. In addition, the proposed model will enable the development of a wide range of analysis and musical services both at personal and at collective level.Source: Software Technologies: Applications and Foundations, edited by Milazzo, Paolo; Varró, Dániel; Wimmer, Manuel, pp. 51–66, 2016
DOI: 10.1007/978-3-319-50230-4_5
Project(s): CIMPLEX
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2016
Journal article
Open Access
A supervised approach for intra-/inter-community interaction prediction in dynamic social networks
Rossetti G., Guidotti R., Miliou I., Pedreschi D., Giannotti F.
Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intracommunity and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.Source: Social Network Analysis and Mining 6 (2016). doi:10.1007/s13278-016-0397-y
DOI: 10.1007/s13278-016-0397-y
Project(s): CIMPLEX, SoBigData
Metrics:
Rossetti G., Guidotti R., Miliou I., Pedreschi D., Giannotti F.
Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intracommunity and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.Source: Social Network Analysis and Mining 6 (2016). doi:10.1007/s13278-016-0397-y
DOI: 10.1007/s13278-016-0397-y
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
Social Network Analysis and Mining | link.springer.com | Social Network Analysis and Mining | ISTI Repository | CNR ExploRA
2016
Journal article
Open Access
Homophilic network decomposition: a community-centric analysis of online social services
Rossetti G., Pappalardo L., Kikas R., Pedreschi D., Giannotti F., Dumas M.
In this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks--Skype, LastFM and Google+--and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+.Source: Social Network Analysis and Mining 6 (2016). doi:10.1007/s13278-016-0411-4
DOI: 10.1007/s13278-016-0411-4
Project(s): CIMPLEX, SoBigData
Metrics:
Rossetti G., Pappalardo L., Kikas R., Pedreschi D., Giannotti F., Dumas M.
In this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks--Skype, LastFM and Google+--and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+.Source: Social Network Analysis and Mining 6 (2016). doi:10.1007/s13278-016-0411-4
DOI: 10.1007/s13278-016-0411-4
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
Social Network Analysis and Mining | link.springer.com | Social Network Analysis and Mining | ISTI Repository | CNR ExploRA
2016
Conference article
Restricted
A novel approach to evaluate community detection algorithms on ground truth
Rossetti G., Pappalardo L., Rinzivillo S.
Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.Source: Complex Networks VII. Proceedings of the 7th Workshop on Complex Networks, pp. 133–144, Dijon, France, 23-25 March 2016
DOI: 10.1007/978-3-319-30569-1_10
Project(s): CIMPLEX, SoBigData
Metrics:
Rossetti G., Pappalardo L., Rinzivillo S.
Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.Source: Complex Networks VII. Proceedings of the 7th Workshop on Complex Networks, pp. 133–144, Dijon, France, 23-25 March 2016
DOI: 10.1007/978-3-319-30569-1_10
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
Studies in Computational Intelligence | link.springer.com | CNR ExploRA
2016
Report
Restricted
Database of female scientists at the 6 targeted Departments of UNIPI
Romano V., Natilli M., Fadda D., Rossetti G., Giannotti F.
The experience gained with the several funded European projects allows us to collect data on female careers but also to identify the context (at institutional level) as a crucial factor in defining the phenomenon of gender equality. The usual approach is to perform a survey or to ask the administration in order to understand how many women are employed at the different levels of the institution at a certain time. The institution obtains a snapshot of the gender equality or, if the study is repeated regularly, a sequence of snapshots that allows gender researchers to perform comparisons and better understand the trends. The aim of the Women Scientific Career Database is to integrate the study of gender equality in the structure of the administration of the institution, in order to build a permanent gender monitor that is automatically updated by the administration. This new approach allows a real time analysis of the gender equality within the institution and, as data are continuously updated, makes it easier to verify how different strategies, laws or regulations can modify the status of gender equality. In order to better understand how the career of a researcher evolves within the institution through years, a lot of different events have to be monitored, like the type of contract and its evolution, and scientific production. The analysis provides statistics aggregated at university level, at department level and personal level in order to give a global picture of the university status and to show how different departments present different behaviors with respect to the gender inequalities. The personal level aggregation wants instead to show how real women scientist can have a successful career. This report describes the realization of the Women Scientific Career Database, the data model, the acquisition procedures and the implementation of first family of indicators and their rendering through a navigable web interface.Source: Project report, TRIGGER, Deliverable D1.8, 2016
Project(s): TRIGGER
Romano V., Natilli M., Fadda D., Rossetti G., Giannotti F.
The experience gained with the several funded European projects allows us to collect data on female careers but also to identify the context (at institutional level) as a crucial factor in defining the phenomenon of gender equality. The usual approach is to perform a survey or to ask the administration in order to understand how many women are employed at the different levels of the institution at a certain time. The institution obtains a snapshot of the gender equality or, if the study is repeated regularly, a sequence of snapshots that allows gender researchers to perform comparisons and better understand the trends. The aim of the Women Scientific Career Database is to integrate the study of gender equality in the structure of the administration of the institution, in order to build a permanent gender monitor that is automatically updated by the administration. This new approach allows a real time analysis of the gender equality within the institution and, as data are continuously updated, makes it easier to verify how different strategies, laws or regulations can modify the status of gender equality. In order to better understand how the career of a researcher evolves within the institution through years, a lot of different events have to be monitored, like the type of contract and its evolution, and scientific production. The analysis provides statistics aggregated at university level, at department level and personal level in order to give a global picture of the university status and to show how different departments present different behaviors with respect to the gender inequalities. The personal level aggregation wants instead to show how real women scientist can have a successful career. This report describes the realization of the Women Scientific Career Database, the data model, the acquisition procedures and the implementation of first family of indicators and their rendering through a navigable web interface.Source: Project report, TRIGGER, Deliverable D1.8, 2016
Project(s): TRIGGER
See at:
triggerproject.eu | CNR ExploRA