  Extremely skewed label distributions are common in real-world node
classification tasks. If not dealt with appropriately, it significantly hurts
the performance of GNNs in minority classes. Due to its practical importance,
there have been a series of recent research devoted to this challenge. Existing
over-sampling techniques smooth the label distribution by generating ``fake''
minority nodes and synthesizing their features and local topology, which
largely ignore the rich information of unlabeled nodes on graphs. In this
paper, we propose UNREAL, an iterative over-sampling method. The first key
difference is that we only add unlabeled nodes instead of synthetic nodes,
which eliminates the challenge of feature and neighborhood generation. To
select which unlabeled nodes to add, we propose geometric ranking to rank
unlabeled nodes. Geometric ranking exploits unsupervised learning in the node
embedding space to effectively calibrates pseudo-label assignment. Finally, we
identify the issue of geometric imbalance in the embedding space and provide a
simple metric to filter out geometrically imbalanced nodes. Extensive
experiments on real-world benchmark datasets are conducted, and the empirical
results show that our method significantly outperforms current state-of-the-art
methods consistent on different datasets with different imbalance ratios.
