  We present very efficient active learning algorithms for link classification
in signed networks. Our algorithms are motivated by a stochastic model in which
edge labels are obtained through perturbations of a initial sign assignment
consistent with a two-clustering of the nodes. We provide a theoretical
analysis within this model, showing that we can achieve an optimal (to whithin
a constant factor) number of mistakes on any graph G = (V,E) such that |E| =
\Omega(|V|^{3/2}) by querying O(|V|^{3/2}) edge labels. More generally, we show
an algorithm that achieves optimality to within a factor of O(k) by querying at
most order of |V| + (|V|/k)^{3/2} edge labels. The running time of this
algorithm is at most of order |E| + |V|\log|V|.
