标题： 图卷积网络的数据增强观点及蒙特卡洛图学习的提出 显示英文标题

标题： Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning

摘要： 如今，对于图卷积网络有两种主要的理解，即在谱域和空间域中。 但两者都缺乏透明性。 在本工作中，我们引入了一种新的理解——数据增强，这比之前的理解更透明。 受此启发，我们提出了一种新的图学习范式——蒙特卡洛图学习（MCGL）。 MCGL的核心思想包括：(1) 数据增强：通过图结构传播训练集的标签并扩展训练集；(2) 模型训练：使用扩展后的训练集训练传统分类器。 我们使用合成数据集来比较MCGL和干净图上的图卷积操作的优势。 此外，我们展示了MCGL在噪声图上对图结构噪声的容忍度比GCN更弱（四个真实世界数据集）。 而且，受MCGL的启发，我们重新分析了GCN在深度过深时性能变差的原因：我们认为主要原因不是主流观点中的过度平滑，而是图结构噪声，并通过实验验证了我们的观点。 代码可在https://github.com/DongHande/MCGL获取。 显示英文摘要

摘要： 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.