Graph Domain Adaptation

Graph domain adaptation (GDA) tackles the challenge of transferring knowledge learned from one graph to another with differing data distributions, aiming to improve performance on tasks like node classification without extensive re-labeling of the target graph. Current research focuses on addressing scenarios with limited or no access to the source graph, employing techniques like spectral augmentation, adversarial learning, and graph structural modifications to align source and target domains. These advancements are significant because they enable the application of graph neural networks (GNNs) to diverse real-world problems where labeled data is scarce or unavailable, impacting fields such as social network analysis, recommendation systems, and scientific data analysis.