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
2013
Contribution to book
Restricted
Anonymity: a comparison between the legal and computer science perspectives
Mascetti S., Monreale A., Ricci A., Gerino A.
Privacy preservation has emerged as a major challenge in ICT. One possible solution for enforcing privacy is to guarantee anonymity. Indeed, ac- cording to international regulations, no restriction is applied to the handling of anonymous data. Consequently, in the past years the notion of anonymity has been extensively studied by two different communities: Law researchers and professionals that propose definitions of privacy regulations, and Computer Scientists attempting to provide technical solutions for enforcing the legal re- quirements. In this contribution we address the problem with an interdisciplinary approach, in the aim to encourage the reciprocal understanding and collaboration between researchers in the two areas. To achieve this, we compare the different notions of anonymity provided in the European data protection Law with the formal models proposed in Computer Science. This analysis allows us to identify the main similarities and differences between the two points of view, hence high- lighting the need for a joint research effort.Source: European Data Protection: Coming of Age, edited by Serge Gutwirth, Ronald Leenes, Paul De Hert, Yves Poullet, pp. 85–115, 2013
DOI: 10.1007/978-94-007-5170-5_4
Metrics:
Mascetti S., Monreale A., Ricci A., Gerino A.
Privacy preservation has emerged as a major challenge in ICT. One possible solution for enforcing privacy is to guarantee anonymity. Indeed, ac- cording to international regulations, no restriction is applied to the handling of anonymous data. Consequently, in the past years the notion of anonymity has been extensively studied by two different communities: Law researchers and professionals that propose definitions of privacy regulations, and Computer Scientists attempting to provide technical solutions for enforcing the legal re- quirements. In this contribution we address the problem with an interdisciplinary approach, in the aim to encourage the reciprocal understanding and collaboration between researchers in the two areas. To achieve this, we compare the different notions of anonymity provided in the European data protection Law with the formal models proposed in Computer Science. This analysis allows us to identify the main similarities and differences between the two points of view, hence high- lighting the need for a joint research effort.Source: European Data Protection: Coming of Age, edited by Serge Gutwirth, Ronald Leenes, Paul De Hert, Yves Poullet, pp. 85–115, 2013
DOI: 10.1007/978-94-007-5170-5_4
Metrics:
See at:
doi.org 
| Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | link.springer.com | CNR ExploRA
2013
Journal article
Restricted
Multidimensional networks: foundations of structural analysis
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. So far, network analysis has focused on the characterization and measurement of local and global properties of graphs, such as diameter, degree distribution, centrality, and so on. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens in monodimensional networks, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. We tested our framework on different real world multidimensional networks, showing the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks.Source: World wide web (Bussum) 16 (2013): 567–593. doi:10.1007/s11280-012-0190-4 ?
DOI: 10.1007/s11280-012-0190-4
Metrics:
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. So far, network analysis has focused on the characterization and measurement of local and global properties of graphs, such as diameter, degree distribution, centrality, and so on. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens in monodimensional networks, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. We tested our framework on different real world multidimensional networks, showing the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks.Source: World wide web (Bussum) 16 (2013): 567–593. doi:10.1007/s11280-012-0190-4 ?
DOI: 10.1007/s11280-012-0190-4
Metrics:
See at:
World Wide Web | www.springerlink.com | CNR ExploRA