Graph Neural Network (GNN) is an emerging technique for graph-based learning
tasks such as node classification. In this work, we reveal the vulnerability of
GNN to the imbalance of node labels. Traditional solutions for imbalanced
classification (e.g. resampling) are ineffective in node classification without
considering the graph structure. Worse still, they may even bring overfitting
or underfitting results due to lack of sufficient prior knowledge. To solve
these problems, we propose a novel graph neural network framework with
curriculum learning (GNN-CL) consisting of two modules. For one thing, we hope
to acquire certain reliable interpolation nodes and edges through the novel
graph-based oversampling based on smoothness and homophily. For another, we
combine graph classification loss and metric learning loss which adjust the
distance between different nodes associated with minority class in feature
space. Inspired by curriculum learning, we dynamically adjust the weights of
different modules during training process to achieve better ability of
generalization and discrimination. The proposed framework is evaluated via
several widely used graph datasets, showing that our proposed model
consistently outperforms the existing state-of-the-art methods.

本文提出了一种基于课程学习的图神经网络框架（GNN-CL），包含两个模块：基于图的过采样和同时调整特征空间中少数类节点间距离的图分类损失和度量学习损失，通过动态调整这两个模块的权重实现更好的泛化和判别能力。该框架在多个广泛使用的图数据集上进行评估，结果表明我们的模型始终优于现有最先进的方法。