  Graph Neural Networks (GNNs) require a large number of labeled graph samples
to obtain good performance on the graph classification task. The performance of
GNNs degrades significantly as the number of labeled graph samples decreases.
To reduce the annotation cost, it is therefore important to develop graph
augmentation methods that can generate new graph instances to increase the size
and diversity of the limited set of available labeled graph samples. In this
work, we propose a novel mixup-based graph augmentation method, Graph Dual
Mixup (GDM), that leverages both functional and structural information of the
graph instances to generate new labeled graph samples. GDM employs a graph
structural auto-encoder to learn structural embeddings of the graph samples,
and then applies mixup to the structural information of the graphs in the
learned structural embedding space and generates new graph structures from the
mixup structural embeddings. As for the functional information, GDM applies
mixup directly to the input node features of the graph samples to generate
functional node feature information for new mixup graph instances. Jointly, the
generated input node features and graph structures yield new graph samples
which can supplement the set of original labeled graphs. Furthermore, we
propose two novel Balanced Graph Sampling methods to enhance the balanced
difficulty and diversity for the generated graph samples. Experimental results
on the benchmark datasets demonstrate that our proposed method substantially
outperforms the state-of-the-art graph augmentation methods when the labeled
graphs are scarce.
