Graph-based models have become increasingly important in various domains, but the limited size and diversity of existing graph datasets often limit their performance. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. Our approach leverages graph edit distance to generate new graphs that are similar to the original ones but exhibit some variation in their structures. To achieve this, we learn the graph edit distance through a comparison of labeled graphs and utilize this knowledge to create graph edit paths between pairs of original graphs. With randomly sampled graphs from a graph edit path, we enrich the training set to enhance the generalization capability of classification models. We demonstrate the effectiveness of our approach on several benchmark datasets and show that it outperforms existing augmentation methods in graph classification tasks.

翻译：图模型已在多个领域变得愈发重要，但现有图数据集的有限规模和多样性常限制其性能。为解决此问题，我们提出EPIC（基于可学习成本的编辑路径插值），一种新的基于插值的图数据集增强方法。该方法利用图编辑距离生成与原始图相似但结构略有变化的新图。为此，我们通过标注图对比学习图编辑距离，并利用此知识在成对原始图之间构建图编辑路径。通过从图编辑路径中随机采样图，我们丰富训练集以提升分类模型的泛化能力。在多个基准数据集上的实验表明，该方法在图分类任务中优于现有增强方法。