Temporal Patterns of Communication in Social Networks

  • Giovanna┬áMiritello

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Giovanna Miritello
    Pages 1-8
  3. Giovanna Miritello
    Pages 9-44
  4. Giovanna Miritello
    Pages 45-84
  5. Giovanna Miritello
    Pages 85-106
  6. Giovanna Miritello
    Pages 107-130
  7. Giovanna Miritello
    Pages 131-143
  8. Back Matter
    Pages 145-153

About this book


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.


Analysis of Social Networks Handling and Mining Massive Datasets Human Communication Patterns Mathematical Modeling of Human Communication Temporal Behavior of Communication Ties

Authors and affiliations

  • Giovanna┬áMiritello
    • 1
  1. 1., Department of MathematicsUniversidad Carlos III de MadridLeganesSpain

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2013
  • Publisher Name Springer, Heidelberg
  • eBook Packages Physics and Astronomy
  • Print ISBN 978-3-319-00109-8
  • Online ISBN 978-3-319-00110-4
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
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