  Node classification is an important research topic in graph learning. Graph
neural networks (GNNs) have achieved state-of-the-art performance of node
classification. However, existing GNNs address the problem where node samples
for different classes are balanced; while for many real-world scenarios, some
classes may have much fewer instances than others. Directly training a GNN
classifier in this case would under-represent samples from those minority
classes and result in sub-optimal performance. Therefore, it is very important
to develop GNNs for imbalanced node classification. However, the work on this
is rather limited. Hence, we seek to extend previous imbalanced learning
techniques for i.i.d data to the imbalanced node classification task to
facilitate GNN classifiers. In particular, we choose to adopt synthetic
minority over-sampling algorithms, as they are found to be the most effective
and stable. This task is non-trivial, as previous synthetic minority
over-sampling algorithms fail to provide relation information for newly
synthesized samples, which is vital for learning on graphs. Moreover, node
attributes are high-dimensional. Directly over-sampling in the original input
domain could generates out-of-domain samples, which may impair the accuracy of
the classifier. We propose a novel framework, GraphSMOTE, in which an embedding
space is constructed to encode the similarity among the nodes. New samples are
synthesize in this space to assure genuineness. In addition, an edge generator
is trained simultaneously to model the relation information, and provide it for
those new samples. This framework is general and can be easily extended into
different variations. The proposed framework is evaluated using three different
datasets, and it outperforms all baselines with a large margin.
