  Prior research has shown that human perception of similarity differs from
mathematical measures in visual comparison tasks, including those involving
directed acyclic graphs. This divergence can lead to missed differences and
skepticism about algorithmic results. To address this, we aim to learn the
structural differences humans detect in graphs visually. We want to visualize
these human-detected differences alongside actual changes, enhancing
credibility and aiding users in spotting overlooked differences. Our approach
aligns with recent research in machine learning capturing human behavior. We
provide a data augmentation algorithm, a dataset, and a machine learning model
to support this task. This work fills a gap in learning differences in directed
acyclic graphs and contributes to better comparative visualizations.
