  We address the problem of classifying the links of signed social networks
given their full structural topology. Motivated by a binary user behaviour
assumption, which is supported by decades of research in psychology, we develop
an efficient and surprisingly simple approach to solve this classification
problem. Our methods operate both within the active and batch settings. We
demonstrate that the algorithms we developed are extremely fast in both
theoretical and practical terms. Within the active setting, we provide a new
complexity measure and a rigorous analysis of our methods that hold for
arbitrary signed networks. We validate our theoretical claims carrying out a
set of experiments on three well known real-world datasets, showing that our
methods outperform the competitors while being much faster.

Machine Learning;Social and Information Networks;Physics and Society