A Linear Time Active Learning Algorithm for Link Classification

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| = Ω(|V |) by querying O(|V |) 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) edge labels. The running time of this algorithm is at most of order |E|+ |V | log |V |.