  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 between
two graphs via edit operations. Moreover, our method introduces a
context-sensitive cost model that accounts for the importance of specific edit
operations formulated through a learning framework. This allows for a more
nuanced transformation process, where the edit distance is not merely
count-based but reflects meaningful graph attributes. With randomly sampled
graphs from the edit path, we enrich the training set to enhance the
generalization capability of classification models. Experimental evaluations
across several benchmark datasets demonstrate that our approach outperforms
existing augmentation techniques in many tasks.
