A Linear Time Active Learning Algorithm for Link Classification -- Full Version --
Machine Learning
2013-03-01 v2 Social and Information Networks
Machine Learning
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| = \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|.
Cite
@article{arxiv.1301.4767,
title = {A Linear Time Active Learning Algorithm for Link Classification -- Full Version --},
author = {Nicolo Cesa-Bianchi and Claudio Gentile and Fabio Vitale and Giovanni Zappella},
journal= {arXiv preprint arXiv:1301.4767},
year = {2013}
}