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Network Completion and Survey Sampling

Steve Hanneke, Eric P. Xing; JMLR W&CP 5:209-215, 2009.

Abstract

We study the problem of learning the topology of an undirected network by observing a random subsample. Specifically, the sample is chosen by randomly selecting a fixed number of vertices, and for each we are allowed to observe all edges it is incident with. We analyze a general formalization of learning from such samples, and derive confidence bounds on the number of differences between the true and learned topologies, as a function of the number of observed mistakes and the algorithm's bias. In addition to this general analysis, we also analyze a variant of the problem under a stochastic block model assumption.



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