Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation

Understand

Graph-structured datasets usually have irregular graph sizes and connectivities, rendering the use of recent data augmentation techniques, such as Mixup, difficult.

• To tackle this challenge, we present the first Mixup-like graph augmentation method at the graph-level called Graph Transplant, which mixes irregular graphs in data space.
• To be well defined on various scales of the graph, our method identifies the sub-structure as a mix unit that can preserve the local information.
• Since the mixup-based methods without special consideration of the context are prone to generate noisy samples, our method explicitly employs the node saliency information to select meaningful subgraphs and adaptively determine the labels.