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Learning Network Dynamics from Tumblr®: A Search for Influential Users

  • Steven MunnEmail author
  • Kang-Yu Ni
  • Jiejun Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

This work offers an original analysis of a unique data set gathered from the blogging website Tumblr by developing and applying a new data driven method for investigating network dynamics. To our knowledge, this is the first effort to analyze the spread of information on Tumblr on a such a large scale, and our method generally applies to networks where nodes have time-evolving states. We start by testing our method on simulated data, then we follow over 50,000 blogs on Tumblr over a year of activity to determine not only which blogs are influential, but more importantly, how these blogs spread their content.

Keywords

Tumblr Dictionary learning Social networks Influence maximization Non-negative matrix factorization 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of California Santa BarbaraSanta BarbaraUSA
  2. 2.HRL LaboratoriesMalibuUSA

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