Graph Markov Neural Networks (GMNN) have recently been proposed to improve
regular graph neural networks (GNN) by including label dependencies into the
semi-supervised node classification task. GMNNs do this in a theoretically
principled way and use three kinds of information to predict labels. Just like
ordinary GNNs, they use the node features and the graph structure but they
moreover leverage information from the labels of neighboring nodes to improve
the accuracy of their predictions. In this paper, we introduce a new dataset
named WikiVitals which contains a graph of 48k mutually referred Wikipedia
articles classified into 32 categories and connected by 2.3M edges. Our aim is
to rigorously evaluate the contributions of three distinct sources of
information to the prediction accuracy of GMNN for this dataset: the content of
the articles, their connections with each other and the correlations among
their labels. For this purpose we adapt a method which was recently proposed
for performing fair comparisons of GNN performance using an appropriate
randomization over partitions and a clear separation of model selection and
model assessment.

本文介绍了一种名为 GMNN 的图马尔可夫神经网络模型用于半监督节点分类，它在节点特征、图结构、邻居节点标签三个方面提高节点标签预测的准确性，并使用 WikiVitals 数据集进行了有效性验证，研究中使用了适当的随机化方法进行模型性能比较和模型选择。