We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to train a fully-connected network jointly with the graph neural network via parameter sharing and interpolation-based regularization. Further, we provide a theoretical analysis of how GraphMix improves the generalization bounds of the underlying graph neural network, without making any assumptions about the "aggregation" layer or the depth of the graph neural networks. We experimentally validate this analysis by applying GraphMix to various architectures such as Graph Convolutional Networks, Graph Attention Networks and Graph-U-Net. Despite its simplicity, we demonstrate that GraphMix can consistently improve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets: Cora-Full, Co-author-CS and Co-author-Physics.

翻译：我们介绍了基于半监督的图形神经网络物体分类的正规化方法GreatMix。 我们提议通过参数共享和内推法正规化,与图形神经网络联合培训一个完全连接的网络。 此外,我们还从理论上分析了GreaphMix如何改进基本图形神经网络的通用界限,而没有对“聚合”层或图形神经网络的深度作出任何假设。我们实验性地验证了这一分析,将GreaphMix应用到图集网络、图集关注网络和图集-U-网络等各种结构中。尽管它很简单,但我们证明GreaphMix可以使用更简单的结构,例如图形革命网络,在以下三个既定的图形基准上不断改进或密切匹配最新业绩:Cora、Citeseer和Pubmed引用网络数据集,以及三个新提议的数据集:Cora-Full、共同作者-CS和共同作者-Physic。