A Graph Data Augmentation Strategy with Entropy Preserving

• URL: http://arxiv.org/abs/2107.06048v1
• Date: Tue, 13 Jul 2021 12:58:32 GMT
• Title: A Graph Data Augmentation Strategy with Entropy Preserving
• Authors: Xue Liu, Dan Sun, Wei Wei
• Abstract summary: We introduce a novel graph entropy definition as a quantitative index to evaluate feature information among a graph. Under considerations of preserving graph entropy, we propose an effective strategy to generate training data using a perturbed mechanism. Our proposed approach significantly enhances the robustness and generalization ability of GCNs during the training process.
• Score: 11.886325179121226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Graph Convolutional Networks (GCNs) proposed by Kipf and Welling are effective models for semi-supervised learning, but facing the obstacle of over-smoothing, which will weaken the representation ability of GCNs. Recently some works are proposed to tackle with above limitation by randomly perturbing graph topology or feature matrix to generate data augmentations as input for training. However, these operations have to pay the price of information structure integrity breaking, and inevitably sacrifice information stochastically from original graph. In this paper, we introduce a novel graph entropy definition as an quantitative index to evaluate feature information diffusion among a graph. Under considerations of preserving graph entropy, we propose an effective strategy to generate perturbed training data using a stochastic mechanism but guaranteeing graph topology integrity and with only a small amount of graph entropy decaying. Extensive experiments have been conducted on real-world datasets and the results verify the effectiveness of our proposed method in improving semi-supervised node classification accuracy compared with a surge of baselines. Beyond that, our proposed approach significantly enhances the robustness and generalization ability of GCNs during the training process.

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