  Recently, data augmentation in the semi-supervised regime, where unlabeled
data vastly outnumbers labeled data, has received a considerable attention. In
this paper, we describe an efficient technique for this task, exploiting a
recent framework we proposed for missing data imputation called graph
imputation neural network (GINN). The key idea is to leverage both supervised
and unsupervised data to build a graph of similarities between points in the
dataset. Then, we augment the dataset by severely damaging a few of the nodes
(up to 80\% of their features), and reconstructing them using a variation of
GINN. On several benchmark datasets, we show that our method can obtain
significant improvements compared to a fully-supervised model, and we are able
to augment the datasets up to a factor of 10x. This points to the power of
graph-based neural networks to represent structural affinities in the samples
for tasks of data reconstruction and augmentation.
