Graph Mixup

Graph Mixup is a data augmentation technique that enhances the performance of Graph Neural Networks (GNNs) by creating synthetic graph data through interpolation of existing graphs and their labels. Current research focuses on developing effective mixup strategies for various graph tasks, including node and graph classification, addressing challenges like uneven label distribution and out-of-distribution generalization. These methods often leverage graph structure information, such as node neighborhoods or subgraphs, to guide the interpolation process, improving GNN robustness and accuracy, particularly in scenarios with limited labeled data. This work has significant implications for improving the efficiency and reliability of GNNs across diverse applications, including those involving social networks, molecular modeling, and time-series analysis.