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Inferring Social Networks from Outbreaks Dana Angluin1
 

Summary: Inferring Social Networks from Outbreaks
Dana Angluin1
, James Aspnes1
, and Lev Reyzin2
1
Department of Computer Science, Yale University
51 Prospect St., New Haven, CT 06511
{dana.angluin, james.aspnes}@yale.edu
2
Yahoo! Research
111 West 40th St. 17th Fl., New York, NY 10018
lreyzin@yahoo-inc.com
Abstract. We consider the problem of inferring the most likely social
network given connectivity constraints imposed by observations of out-
breaks within the network. Given a set of vertices (or agents) V and
constraints (or observations) Si V we seek to find a minimum log-
likelihood cost (or maximum likelihood) set of edges (or connections) E
such that each Si induces a connected subgraph of (V, E). For the offline
version of the problem, we prove an (log(n)) hardness of approxima-
tion result for uniform cost networks and give an algorithm that almost

  

Source: Aspnes, James - Department of Computer Science, Yale University

 

Collections: Computer Technologies and Information Sciences