Interactive information visualization in a conference location Maria Chiara Caschera, Fernando Ferri, Patrizia Grifoni Istituto di Ricerche sulla Popolazione e Politiche Sociali, CNR, Via Nizza 128, 00198 Roma, Italy {mc.caschera, fernando.ferri, patrizia.grifoni}@irpps.cnr.it 1 Introduction The growing interest in visualization and analysis of social networks has led to the development of several methods of structural analysis in order to explore and analyse individual and group behaviors. An important component in the visualization of social networks is the understanding of the spatial and the temporal characteristics of individual and group behaviors. In particular, the increasing importance of social networks is due to the decentralization of people working surroundings and asynchronous work timings. This is connected to the fact that people are able to communicate by mobile devices with others at anytime and any-where and they are free from the restrictions of time and place. Moreover the interest in visualization and analysis of social networks is supported by the diffusion of the pervasive computing technologies, that allow to infer human activities through different sensors and collecting their data. These data can be refereed to location, movement orientations, work interests of the users and so on. The collection of these data can be used to infer users’ behaviors and to offer them services that they could need. Considering social network application scenario, these collected data can be used in order to support the discovery of people with the same interests about work and hobbies and to facilitate their interaction. Visualization can play an important role for social network analysis. The net can be created mainly using two approaches: using graph-based approaches that consist of nodes and edges that connect the different nodes; and using matrix, where row and columns stand for people and properties, and the numbers in each cell stand for specific relationships among these values. In detail, most social network applications, based on graph visualization [1], represent social actors (persons or groups) as nodes, while edges show connections among the actors or flows between them. A graph-based visualization is used in [2] to highlight clusters of contacts derived from email archives showing different aspects of a person’s social network. However the graph representation is not always appropriate for large or dense data about social networks because of their complexity. In this case a good alternative can be given by the matrix representation.