Inferring Social Networks from Outbreaks

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Inferring Social Networks from Outbreaks
We consider the problem of inferring the most likely social network given connectivity constraints imposed by observations of outbreaks within the network. Given a set of vertices (or agents) V and constraints (or observations) Si V we seek to find a minimum loglikelihood 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 approximation result for uniform cost networks and give an algorithm that almost matches this bound, even for arbitrary costs. Then we consider the online problem, where the constraints are satisfied as they arrive. We give an O(n log(n))-competitive algorithm for the arbitrary cost online problem, which has an (n)-competitive lower bound. We look at the uniform cost case as well and give an O(n2/3 log2/3 (n))-competitive algorithm against an oblivious adversary, as well as an ( n)-competitive lower bound against an adaptive ad...
Dana Angluin, James Aspnes, Lev Reyzin
Added 26 Oct 2010
Updated 26 Oct 2010
Type Conference
Year 2010
Where ALT
Authors Dana Angluin, James Aspnes, Lev Reyzin
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