  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.
