  Graph classification, which aims to identify the category labels of graphs,
plays a significant role in drug classification, toxicity detection, protein
analysis etc. However, the limitation of scale in the benchmark datasets makes
it easy for graph classification models to fall into over-fitting and
undergeneralization. To improve this, we introduce data augmentation on graphs
(i.e. graph augmentation) and present four methods:random mapping,
vertex-similarity mapping, motif-random mapping and motif-similarity mapping,
to generate more weakly labeled data for small-scale benchmark datasets via
heuristic transformation of graph structures. Furthermore, we propose a generic
model evolution framework, named M-Evolve, which combines graph augmentation,
data filtration and model retraining to optimize pre-trained graph classifiers.
Experiments on six benchmark datasets demonstrate that the proposed framework
helps existing graph classification models alleviate over-fitting and
undergeneralization in the training on small-scale benchmark datasets, which
successfully yields an average improvement of 3 - 13% accuracy on graph
classification tasks.
