  The complexity and non-Euclidean structure of graph data hinder the
development of data augmentation methods similar to those in computer vision.
In this paper, we propose a feature augmentation method for graph nodes based
on topological regularization, in which topological structure information is
introduced into end-to-end model. Specifically, we first obtain topology
embedding of nodes through unsupervised representation learning method based on
random walk. Then, the topological embedding as additional features and the
original node features are input into a dual graph neural network for
propagation, and two different high-order neighborhood representations of nodes
are obtained. On this basis, we propose a regularization technique to bridge
the differences between the two different node representations, eliminate the
adverse effects caused by the topological features of graphs directly used, and
greatly improve the performance. We have carried out extensive experiments on a
large number of datasets to prove the effectiveness of our model.
