  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.
