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Mobile Phone Data for Inferring Social Network Structure

  • Nathan Eagle
  • Alex (Sandy) Pentland
  • David Lazer

Abstract

We analyze 330,000 hours of continuous behavioral data logged by the mobile phones of 94 subjects, and compare these observations with self-report relational data. The information from these two data sources is overlapping but distinct, and the accuracy of self-report data is considerably affected by such factors as the recency and salience of particular interactions. We present a new method for precise measurements of large-scale human behavior based on contextualized proximity and communication data alone, and identify characteristic behavioral signatures of relationships that allowed us to accurately predict 95% of the reciprocated friendships in the study. Using these behavioral signatures we can predict, in turn, individual-level outcomes such as job satisfaction.

Keywords

Social Network Mobile Phone Social Network Analysis Friendship Network Social Network Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Nathan Eagle
    • 1
  • Alex (Sandy) Pentland
    • 2
  • David Lazer
    • 3
  1. 1.MIT Design LaboratoryMassachusetts Institute of TechnologyCambridge
  2. 2.MIT Media LaboratoryMassachusetts Institute of TechnologyCambridge
  3. 3.Kennedy School of Government, Harvard UniversityCambridge

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