  Node classification is a fundamental graph-based task that aims to predict
the classes of unlabeled nodes, for which Graph Neural Networks (GNNs) are the
state-of-the-art methods. Current GNNs assume that nodes in the training set
contribute equally during training. However, the quality of training nodes
varies greatly, and the performance of GNNs could be harmed by two types of
low-quality training nodes: (1) inter-class nodes situated near class
boundaries that lack the typical characteristics of their corresponding
classes. Because GNNs are data-driven approaches, training on these nodes could
degrade the accuracy. (2) mislabeled nodes. In real-world graphs, nodes are
often mislabeled, which can significantly degrade the robustness of GNNs. To
mitigate the detrimental effect of the low-quality training nodes, we present
CLNode, which employs a selective training strategy to train GNN based on the
quality of nodes. Specifically, we first design a multi-perspective difficulty
measurer to accurately measure the quality of training nodes. Then, based on
the measured qualities, we employ a training scheduler that selects appropriate
training nodes to train GNN in each epoch. To evaluate the effectiveness of
CLNode, we conduct extensive experiments by incorporating it in six
representative backbone GNNs. Experimental results on real-world networks
demonstrate that CLNode is a general framework that can be combined with
various GNNs to improve their accuracy and robustness.
