Recovering social networks from contagion information

Sucheta Soundarajan, John E. Hopcroft

Research output: Chapter in Book/Entry/PoemConference contribution

5 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationTheory and Applications of Models of Computation - 7th Annual Conference, TAMC 2010, Proceedings
Number of pages12
StatePublished - 2010
Externally publishedYes
Event7th Annual Conference on Theory and Applications of Models of Computation, TAMC 2010 - Prague, Czech Republic
Duration: Jun 7 2010Jun 11 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6108 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th Annual Conference on Theory and Applications of Models of Computation, TAMC 2010
Country/TerritoryCzech Republic


  • Contagion
  • Diffusion
  • Graph Algorithms
  • Social Networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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