A Linear Time Active Learning Algorithm for Link Classification

Abstract

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| is at least order of |V|^3/2 by querying at most order of |V|^3/2 edge labels. More generally, we show an algorithm that achieves optimality to within a factor of order 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||V|.

Tasks

Reproductions