  Data imbalance is easily found in annotated data when the observations of
certain continuous label values are difficult to collect for regression tasks.
When they come to molecule and polymer property predictions, the annotated
graph datasets are often small because labeling them requires expensive
equipment and effort. To address the lack of examples of rare label values in
graph regression tasks, we propose a semi-supervised framework to progressively
balance training data and reduce model bias via self-training. The training
data balance is achieved by (1) pseudo-labeling more graphs for
under-represented labels with a novel regression confidence measurement and (2)
augmenting graph examples in latent space for remaining rare labels after data
balancing with pseudo-labels. The former is to identify quality examples from
unlabeled data whose labels are confidently predicted and sample a subset of
them with a reverse distribution from the imbalanced annotated data. The latter
collaborates with the former to target a perfect balance using a novel
label-anchored mixup algorithm. We perform experiments in seven regression
tasks on graph datasets. Results demonstrate that the proposed framework
significantly reduces the error of predicted graph properties, especially in
under-represented label areas.
