  Motivated by social balance theory, we develop a theory of link
classification in signed networks using the correlation clustering index as
measure of label regularity. We derive learning bounds in terms of correlation
clustering within three fundamental transductive learning settings: online,
batch and active. Our main algorithmic contribution is in the active setting,
where we introduce a new family of efficient link classifiers based on covering
the input graph with small circuits. These are the first active algorithms for
link classification with mistake bounds that hold for arbitrary signed
networks.
