We present GraphMix, a regularization technique for Graph Neural Network based semi-supervised object classification, leveraging the recent advances in the regularization of classical deep neural networks. Specifically, we propose a unified approach in which we train a fully-connected network jointly with the graph neural network via parameter sharing, interpolation-based regularization, and self-predicted-targets. Our proposed method is architecture agnostic in the sense that it can be applied to any variant of graph neural networks which applies a parametric transformation to the features of the graph nodes. Despite its simplicity, with GraphMix we can consistently improve results and achieve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: the 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:我们展示了基于图形神经网络的半受监督物体分类正规化技术“图形Mix”,利用古典深神经网络正规化的最新进展。具体地说,我们提出了一个统一的方法,通过参数共享、基于内插的正规化和自我预测的目标,与图形神经网络共同培训一个完全连接的网络。我们提议的方法是建筑学的不可知性,因为它可以适用于任何对图形节点特征进行参数转换的图形神经网络的变异。尽管它很简单,但用“图形Mix”我们可以不断改进结果并实现或密切匹配最新性能,甚至使用更简单的结构,例如图形革命网络,跨越三个既定的图形基准:科拉、台岩和普布姆热引用网络数据集,以及三个新提议的数据集:科拉-富尔、共同作者-CS和共同作者-Physic。