Trade-Offs in Social and Behavioral Modeling in Mobile Networks

  • Yaniv Altshuler
  • Michael Fire
  • Nadav Aharony
  • Zeev Volkovich
  • Yuval Elovici
  • Alex (Sandy) Pentland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7812)


Mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Today’s smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and behavior prediction. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones. In particular, we look at the dynamic learning process over time with various sizes of sampling groups and examine the interplay between these two parameters. We validate our model using extensive simulations carried out using the "Friends and Family" dataset which contains rich data signals gathered from the smartphones of 140 adult members of a young-family residential community for over a year and is one of the most comprehensive mobile phone datasets gathered in academia to date.


Machine Learning Social Networks Mobile Networks 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yaniv Altshuler
    • 1
  • Michael Fire
    • 2
  • Nadav Aharony
    • 1
  • Zeev Volkovich
    • 3
  • Yuval Elovici
    • 2
  • Alex (Sandy) Pentland
    • 1
  1. 1.MIT Media LabUSA
  2. 2.Deutsche Telekom Lab, Department of Information Systems EngineeringBen-Gurion UniversityIsrael
  3. 3.Department of Software EngineeringOrt Braude CollegeIsrael

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