Explanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning
This addresses a bottleneck in semi-supervised graph representation learning for domains like network analysis, though it is incremental as it builds on existing augmentation methods.
The paper tackles the problem of suboptimal graph representation learning due to semantics-agnostic augmentations by proposing Explanation-Preserving Augmentation (EPA), which uses graph explanations to generate semantics-preserving augmentations, and shows that EPA-GRL outperforms state-of-the-art methods on benchmark datasets.
Self-supervised graph representation learning (GRL) typically generates paired graph augmentations from each graph to infer similar representations for augmentations of the same graph, but distinguishable representations for different graphs. While effective augmentation requires both semantics-preservation and data-perturbation, most existing GRL methods focus solely on data-perturbation, leading to suboptimal solutions. To fill the gap, in this paper, we propose a novel method, Explanation-Preserving Augmentation (EPA), which leverages graph explanation for semantics-preservation. EPA first uses a small number of labels to train a graph explainer, which infers the subgraphs that explain the graph's label. Then these explanations are used for generating semantics-preserving augmentations for boosting self-supervised GRL. Thus, the entire process, namely EPA-GRL, is semi-supervised. We demonstrate theoretically, using an analytical example, and through extensive experiments on a variety of benchmark datasets, that EPA-GRL outperforms the state-of-the-art (SOTA) GRL methods that use semantics-agnostic augmentations. The code is available at https://github.com/realMoana/EPA-GRL.