Temporal Patterns of Communication in Social Networks [electronic resource] / by Giovanna Miritello.
Contributor(s): SpringerLink (Online service).Material type: TextSeries: Springer Theses, Recognizing Outstanding Ph.D. Research: Publisher: Heidelberg : Springer International Publishing : Imprint: Springer, 2013Description: XIV, 153 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319001104.Subject(s): Physics | Game theory | System theory | Mathematics | Social sciences | Communication | Physics | Complex Networks | Mathematics in the Humanities and Social Sciences | Communication Studies | Complex Systems | Game Theory, Economics, Social and Behav. Sciences | Física y Astronomía | Física y AstronomíaAdditional physical formats: Printed edition:: No titleDDC classification: 621 Online resources: Texto completo
|Item type||Current location||Shelving location||Call number||Status||Date due||Barcode||Item holds|
|Springer (Colección 2013)||BIBLIOTECA GENERAL||Física y Astronomía||Física y Astronomía (Browse shelf)||Available|
Introduction and Motivation -- Social and Communication Networks -- Social Strategies in Communication Networks -- Predicting Tie Creation and Decay -- Information Spreading on Communication Networks -- Conclusion, contributions and vision for the future -- Data and Materials.
The main interest of this research has been in understanding and characterizing large networks of human interactions as continuously changing objects. In fact, although many real social networks are dynamic networks whose elements and properties continuously change over time, traditional approaches to social network analysis are essentially static, thus neglecting all temporal aspects. Specifically, we have investigated the role that temporal patterns of human interaction play in three main fields of social network analysis and data mining: characterization of time (or attention) allocation in social networks, prediction of link decay/persistence, and information spreading. In order to address this we analyzed large anonymized data sets of phone call communication traces over long periods of time. Access to these observations was granted by Telefonica Research, Spain. The findings that emerge from our research indicate that the observed heterogeneities and correlations of human temporal patterns of interaction significantly affect the traditional view of social networks, shifting from a very steady to a highly complex entity. Since structure and dynamics are tightly coupled, they cannot be disentangled in the analysis and modeling of human behavior, though traditional models seek to do so. Our results impact not only the way in which social network are traditionally characterized, but more importantly also the understanding and modeling phenomena such as group formation, spread of epidemics, and the dissemination of ideas, opinions and information.