ENGAGE：用于图表示学习的解释引导数据增强
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
AI总结：
针对图对比学习中随机扰动易破坏图结构的问题，提出解释引导的数据增强方法ENGAGE，利用平滑激活图评估节点重要性并设计结构与特征增强方案，有效提升了图表示学习性能。
摘要：
最近的对比学习方法因其有效的表示学习能力，被广泛应用于图数据建模。随机扰动常被用于构建图数据的对比视图，但这可能会意外破坏图结构并导致次优性能。此外，图数据通常高度抽象，难以提取直观含义并设计更具信息量的增强方案。有效的表示应保留数据的关键特征并舍弃冗余信息。本文提出ENGAGE（ExplaNation Guided data AuGmEntation），通过解释引导对比增强过程，以保留图中的关键部分并探索移除冗余信息。具体而言，我们设计了一种名为平滑激活图的高效无监督解释方法，作为表示学习中节点重要性的指示器。随后，我们设计了两种图数据增强方案，分别用于扰动结构和特征信息。我们还在信息论框架下为所提方法提供了理论证明。我们在不同模型架构和多种真实图上进行了图级和节点级任务实验，证明了ENGAGE的有效性和灵活性。ENGAGE代码可在https://github.com/sycny/ENGAGE获取。
英文摘要：
The recent contrastive learning methods, due to their effectiveness in representation learning, have been widely applied to modeling graph data. Random perturbation is widely used to build contrastive views for graph data, which however, could accidentally break graph structures and lead to suboptimal performance. In addition, graph data is usually highly abstract, so it is hard to extract intuitive meanings and design more informed augmentation schemes. Effective representations should preserve key characteristics in data and abandon superfluous information. In this paper, we propose ENGAGE (ExplaNation Guided data AuGmEntation), where explanation guides the contrastive augmentation process to preserve the key parts in graphs and explore removing superfluous information. Specifically, we design an efficient unsupervised explanation method called smoothed activation map as the indicator of node importance in representation learning. Then, we design two data augmentation schemes on graphs for perturbing structural and feature information, respectively. We also provide justification for the proposed method in the framework of information theories. Experiments of both graph-level and node-level tasks, on various model architectures and on different real-world graphs, are conducted to demonstrate the effectiveness and flexibility of ENGAGE. The code of ENGAGE can be found: https://github.com/sycny/ENGAGE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
