Explanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning


Publication date: 16 Oct 2024

Topic: Representation Learning

Paper: https://arxiv.org/pdf/2410.12657v1.pdf

GitHub: https://github.com/junxia97/simgrace

Description:

In this paper, we propose a novel method, Explanation-Preserving Augmentation (EPA), that leverages graph explanation techniques for generating augmented graphs that can bridge the gap between semantics-preservation and data-perturbation. EPA first uses a small number of labels to train a graph explainer to infer sub-structures (explanations) that are most relevant to a graph's semantics. These explanations are then used to generate semantics-preserving augmentations for self-supervised GRL, namely EPA-GRL. 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, which are built upon semantics-agnostic data augmentations.

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