EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
Jaeseung Heo Seungbeom Lee Sungsoo Ahn Dongwoo Kim
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.