Semi-supervised classification try to take advantage of a large amount of unlabeled data in addition to a small amount of labeled data, in order to achieve good classification accuracy while reducing the cost of manually annotating data.
graph-based techniques for semi-supervised classification (Zhou et al., 2004;Zhu et al., 2003;Callut et al., 2008;Wang et al., 2008) are recognized as a promising approach.