Motif-aware curriculum learning for node classification.

Node classification, seeking to predict the categories of unlabeled nodes, is a crucial task in graph learning. One of the most popular methods for node classification is currently Graph Neural Networks (GNNs). However, conventional GNNs assign equal importance to all training nodes, which can lead to a reduction in accuracy and robustness due to the influence of complex nodes information. In light of the potential benefits of curriculum learning, some studies have proposed to incorporate curriculum learning into GNNs , where the node information can be acquired in an orderly manner. Nevertheless, the existing curriculum learning-based node classification methods fail to consider the subgraph structural information. To address this issue, we propose a novel approach, Motif-aware Curriculum Learning for Node Classification (MACL). It emphasizes the role of motif structures within graphs to fully utilize subgraph information and measure the quality of nodes, supporting an organized learning process for GNNs. Specifically, we design a motif-aware difficulty measurer to evaluate the difficulty of training nodes from different perspectives. Furthermore, we have implemented a training scheduler to introduce appropriate training nodes to the GNNs at suitable times. We conduct extensive experiments on five representative datasets. The results show that incorporating MACL into GNNs can improve the accuracy.