Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks

Understand

In recent years, graph neural networks (GNNs) have achieved state-of-the-art performance for node classification.

• However, most existing GNNs would suffer from the graph imbalance problem.
• In many real-world scenarios, node classes are imbalanced, with some majority classes making up most parts of the graph.
• The message propagation mechanism in GNNs would further amplify the dominance of those majority classes, resulting in sub-optimal classification performance.