Class-Balanced and Reinforced Active Learning on Graphs

Understand

Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification.

• Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a lower cost.
• However, most existing algorithms for reinforced active learning in GNNs may lead to a highly imbalanced class distribution, especially in highly skewed class scenarios.
• GNNs trained with class-imbalanced labeled data are susceptible to bias toward majority classes, and the lower performance of minority classes may lead to a decline in overall performance.