  Recent years have witnessed great success in handling node classification
tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based
on the assumption that node samples for different classes are balanced, while
for many real-world graphs, there exists the problem of class imbalance, i.e.,
some classes may have much fewer samples than others. In this case, directly
training a GNN classifier with raw data would under-represent samples from
those minority classes and result in sub-optimal performance. This paper
presents GraphMixup, a novel mixup-based framework for improving
class-imbalanced node classification on graphs. However, directly performing
mixup in the input space or embedding space may produce out-of-domain samples
due to the extreme sparsity of minority classes; hence we construct semantic
relation spaces that allows the Feature Mixup to be performed at the semantic
level. Moreover, we apply two context-based self-supervised techniques to
capture both local and global information in the graph structure and then
propose Edge Mixup specifically for graph data. Finally, we develop a
\emph{Reinforcement Mixup} mechanism to adaptively determine how many samples
are to be generated by mixup for those minority classes. Extensive experiments
on three real-world datasets show that GraphMixup yields truly encouraging
results for class-imbalanced node classification tasks.

Machine Learning;Artificial Intelligence