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Active Learning for Community Detection in Stochastic Block Models
Our main result is to show that, under certain conditions, sampling the labels of a vanishingly small fraction of nodes (a number sub-linear in $n$) is sufficient for exact community detection even when $D(a, b)<1$.
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches
A new sampling algorithm is proposed, which sequentially selects the graph nodes to be sampled, based on an aggressive search for the boundary of the signal over the graph.
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