Transferable Graph Condensation from the Causal Perspective

A novel method for graph dataset condensation, named TGCC, has been proposed to address the challenges posed by the increasing scale of graph datasets in representation learning. This method utilizes causal-invariance principles to create smaller, information-rich datasets that maintain performance across various tasks and domains.

The introduction of TGCC is significant as it enhances the flexibility and applicability of graph representation learning, allowing for improved performance in diverse scenarios without the constraints of matching original datasets and tasks.