Imbalanced Graph

Imbalanced graph learning addresses the challenge of biased predictions arising from uneven class distributions in graph data, a common problem across various domains. Current research focuses on developing novel algorithms and model architectures, such as graph neural networks (GNNs) with integrated oversampling techniques or topological augmentations, to improve the representation and classification of under-represented classes without relying solely on class rebalancing. These efforts aim to enhance the robustness and fairness of graph-based machine learning models, leading to more reliable and accurate predictions in applications ranging from molecule property prediction to social network analysis.