Adversarial Graph Contrastive Learning

Adversarial graph contrastive learning (AGCL) aims to learn robust graph representations by leveraging contrastive learning while simultaneously defending against adversarial attacks on graph structure and node features. Current research focuses on developing methods to improve the robustness of AGCL models, including techniques like adversarial training, randomized smoothing, and self-attentive mechanisms within dual-stream architectures. These advancements are significant because they enhance the reliability and generalizability of graph neural networks in applications sensitive to noisy or manipulated data, such as emotion recognition from EEG signals and molecular property prediction.