Graph Transfer Learning

Graph transfer learning aims to leverage knowledge from data-rich source graphs to improve performance on data-scarce target graphs, addressing the limitations of training graph neural networks (GNNs) from scratch. Current research focuses on developing robust methods to handle noisy labels, domain shifts (differences in graph structure and attributes), and limited labeled data, often employing techniques like adversarial domain adaptation, graph contrastive learning, and prompt engineering within GNN architectures. These advancements are significant for various applications, including traffic prediction, urban computing, and recommendation systems, where data scarcity is a common challenge, enabling more accurate and efficient models in real-world scenarios.