### 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.

Original language | English (US) |
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Title of host publication | Theory and Applications of Models of Computation - 7th Annual Conference, TAMC 2010, Proceedings |

Pages | 419-430 |

Number of pages | 12 |

DOIs | |

State | Published - Jul 15 2010 |

Externally published | Yes |

Event | 7th Annual Conference on Theory and Applications of Models of Computation, TAMC 2010 - Prague, Czech Republic Duration: Jun 7 2010 → Jun 11 2010 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 6108 LNCS |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 7th Annual Conference on Theory and Applications of Models of Computation, TAMC 2010 |
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Country | Czech Republic |

City | Prague |

Period | 6/7/10 → 6/11/10 |

### Keywords

- Contagion
- Diffusion
- Graph Algorithms
- Social Networks

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

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## Cite this

Soundarajan, S., & Hopcroft, J. E. (2010). Recovering social networks from contagion information. In

*Theory and Applications of Models of Computation - 7th Annual Conference, TAMC 2010, Proceedings*(pp. 419-430). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6108 LNCS). https://doi.org/10.1007/978-3-642-13562-0_38