June 5, 2024, 4:45 a.m. | Jaeseung Heo, Seungbeom Lee, Sungsoo Ahn, Dongwoo Kim

cs.LG updates on arXiv.org arxiv.org

arXiv:2306.01310v2 Announce Type: replace
Abstract: Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structure. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. To interpolate between two graphs lying in an irregular domain, EPIC leverages the concept of graph edit distance, constructing an edit path that represents the transformation process …

abstract arxiv augmentation cost cs.ai cs.lg data domains edit epic graph graph data improving interpolation issue novel path performance replace role type via

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