Recovering social networks from contagion information

  • Authors:
  • Sucheta Soundarajan;John E. Hopcroft

  • Affiliations:
  • Dept of Computer Science, Cornell University;Dept of Computer Science, Cornell University

  • Venue:
  • TAMC'10 Proceedings of the 7th annual conference on Theory and Applications of Models of Computation
  • Year:
  • 2010

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Abstract

Many algorithms for analyzing social networks assume that the structure of the network is known, but this is not always a reasonable assumption We wish to reconstruct an underlying network given data about how some property, such as disease, has spread through the network Properties may spread through a network in different ways: for instance, an individual may learn information as soon as one of his neighbors has learned that information, but political beliefs may follow a different type of model We create algorithms for discovering underlying networks that would give rise to the diffusion in these models.