Trade-offs in social and behavioral modeling in mobile networks

Yaniv Altshuler, Michael Fire, Nadav Aharony, Zeev Volkovich, Yuval Elovici, Alex Pentland

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationSocial Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings
Number of pages12
StatePublished - 14 Mar 2013
Event6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013 - Washington, DC, United States
Duration: 2 Apr 20135 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7812 LNCS


Conference6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013
Country/TerritoryUnited States
CityWashington, DC


  • Machine Learning
  • Mobile Networks
  • Social Networks

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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