Towards fidelity of graph data augmentation via equivariance

Published: 01 Jan 2023, Last Modified: 07 Aug 2024Knowl. Based Syst. 2023EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•The first study that applies equivariance to the field of graph data augmentation.•We theoretically prove that the GCN is equivariant to our Sg and Tg transformations.•Simultaneously obtain the augmented features, topology structure and the labels.•Does not blur the features and topological structure of original nodes.
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