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Systematic Dynamic and Heterogeneous Analysis of Rich Social Network Data

  • Lei MengEmail author
  • Tijana Milenković
  • Aaron Striegel
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 549)

Abstract

Recent technological advances have lead to increasing amounts of social network data that is longitudinal or encompasses multiple link types.We aim to provide a framework for systematic analysis of such data. We validate the framework on a unique and rich social network, by studying the evolution of network structure over an 18-month period as well as the relationships between different communication types (including both digital (e.g., Facebook) and face-to-face interactions).

Keywords

Time Slot Communication Type Online Social Network Link Prediction Network Type 
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 International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA

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