论文标题
图形神经网络的分层模型选择
Hierarchical Model Selection for Graph Neural Netoworks
论文作者
论文摘要
图形数据上的节点分类是一个主要问题，并且已经提出了各种图形神经网络（GNN）。 H2GCN和CPF等GNN的变体通过改善传统GNN的弱点来优于图形卷积网络（GCN）。但是，在节点分类任务中，这些GNN变体与其他GNN相比，这些GNN变体无法表现良好。这是因为H2GCN具有高平均程度的图形数据的特征稀疏，并且CPF引起了有关标签 - 传播适用性的问题。因此，我们提出了一个分层模型选择框架（HMSF），该框架通过分析每个图数据的指标来选择适当的GNN模型。在实验中，我们表明，由HMSF选择的模型可以在各种类型的图形数据上在节点分类上实现高性能。
Node classification on graph data is a major problem, and various graph neural networks (GNNs) have been proposed. Variants of GNNs such as H2GCN and CPF outperform graph convolutional networks (GCNs) by improving on the weaknesses of the traditional GNN. However, there are some graph data which these GNN variants fail to perform well than other GNNs in the node classification task. This is because H2GCN has a feature thinning on graph data with high average degree, and CPF gives rise to a problem about label-propagation suitability. Accordingly, we propose a hierarchical model selection framework (HMSF) that selects an appropriate GNN model by analyzing the indicators of each graph data. In the experiment, we show that the model selected by our HMSF achieves high performance on node classification for various types of graph data.
