When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision

Understand

This paper studies active learning (AL) on graphs, whose purpose is to discover the most informative nodes to maximize the performance of graph neural networks (GNNs).

• Previously, most graph AL methods focus on learning node representations from a carefully selected labeled dataset with large amount of unlabeled data neglected.
• Motivated by the success of contrastive learning (CL), we propose a novel paradigm that seamlessly integrates graph AL with CL.
• While being able to leverage the power of abundant unlabeled data in a self-supervised manner, nodes selected by AL further provide semantic information that can better guide representation learning.