Structural features are important features in graph datasets. However, although there are some correlation analysis of features based on covariance, there is no relevant research on exploring structural feature correlation on graphs with graph neural network based models. In this paper, we introduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation, which is based on graph neural network. The results show that there exists high correlation between some of the structural features. A redundant feature combination with initial node features, which is filtered by graph neural network has improved its classification accuracy in some graph datasets. We compare the difference between concatenation methods on connecting embeddings between features and show that the simplest is the best. We generalize on the synthetic geometric graphs and certify the results on prediction difficulty between two structural features.
翻译:图表数据集中的结构特征是重要的特征。 但是,尽管根据共差对各种特征进行了一些相关分析,但在探索图形神经网络模型中的图形结构特征相关性方面没有相关的研究。 在本文中,我们引入了低维空间中(Fea2Fea)预测管道的图形特征,以探索基于图形神经网络的结构性特征相关性的一些初步结果。结果显示,一些结构特征之间存在高度的关联性。由图形神经网络过滤的与初始节点特征的冗余特征结合提高了图形神经网络在一些图形数据集中的分类准确性。我们比较了连接各特征间嵌入的组合方法之间的差异,并表明最简单的是最好的。我们概括了合成几何图形,并验证了两个结构特征之间预测难度的结果。