  In network science, the null model is typically used to generate a series of
graphs based on randomization as a term of comparison to verify whether a
network in question displays some non-trivial features such as community
structure. Since such non-trivial features play a significant role in graph
classification, the null model could be useful for network data augmentation to
enhance classification performance. In this paper, we propose a novel technique
that combines the null model with data augmentation for graph classification.
Moreover, we propose four standard null model-based augmentation methods and
four approximate null model-based augmentation methods to verify and improve
the performance of our graph classification technique. Our experiments
demonstrate that the proposed augmentation technique has significantly achieved
general improvement on the tested datasets. In addition, we find that the
standard null model-based augmentation methods always outperform the
approximate ones, depending on the design mechanisms of the null models. Our
results indicate that the choice of non-trivial features is significant for
increasing the performance of augmentation models for different network
structures, which also provides a new perspective of data augmentation for
studying various graph classification methods.
