Topological Regularization for Graph Neural Networks Augmentation
This work addresses a bottleneck in graph machine learning by enabling more effective data augmentation, though it is incremental as it builds on existing graph neural network techniques.
The paper tackles the challenge of data augmentation for graph data by proposing a feature augmentation method using topological regularization, which improves performance on graph neural networks across multiple datasets.
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.