论文标题
图形卷积网络上的数据增强视图和蒙特卡洛图学习的建议
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
论文作者
论文摘要
如今，对图形卷积网络有两个主要理解，即在光谱和空间域中。但是两者都缺乏透明度。在这项工作中，我们对IT介绍了一种新的理解 - 数据扩展，这比以前的理解更透明。受其启发，我们提出了一个新的图形学习范式-Monte Carlo Graph Learning（MCGL）。 MCGL的核心思想包含：（1）数据增强：通过图形结构设置训练的标签并扩展训练集； （2）模型培训：使用扩展的培训集来培训传统分类器。我们使用合成数据集比较清洁图上MCGL和图形卷积操作的强度。此外，我们表明MCGL对图形结构噪声的容忍度比嘈杂图（四个现实世界数据集）上的GCN弱。此外，受到MCGL的启发，我们重新分析了GCN的性能在加深太多时会变得更糟的原因：而不是过度光滑的主流观点，我们认为主要原因是图形结构噪声，并在实验上验证了我们的观点。该代码可在https://github.com/donghande/mcgl上找到。
Today, there are two major understandings for graph convolutional networks, i.e., in the spectral and spatial domain. But both lack transparency. In this work, we introduce a new understanding for it -- data augmentation, which is more transparent than the previous understandings. Inspired by it, we propose a new graph learning paradigm -- Monte Carlo Graph Learning (MCGL). The core idea of MCGL contains: (1) Data augmentation: propagate the labels of the training set through the graph structure and expand the training set; (2) Model training: use the expanded training set to train traditional classifiers. We use synthetic datasets to compare the strengths of MCGL and graph convolutional operation on clean graphs. In addition, we show that MCGL's tolerance to graph structure noise is weaker than GCN on noisy graphs (four real-world datasets). Moreover, inspired by MCGL, we re-analyze the reasons why the performance of GCN becomes worse when deepened too much: rather than the mainstream view of over-smoothing, we argue that the main reason is the graph structure noise, and experimentally verify our view. The code is available at https://github.com/DongHande/MCGL.
