 
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@yahooinc.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
