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.

翻译：先前的研究表明，在视觉比较任务（包括涉及有向无环图的任务）中，人类对相似性的感知与数学度量存在差异。这种分歧可能导致遗漏差异以及对算法结果的怀疑。为解决这一问题，我们的目标是学习人类在视觉上从图中检测到的结构差异。我们希望将这些人类检测到的差异与实际变化一同可视化，以增强可信度并帮助用户发现被忽视的差异。我们的方法与近期机器学习中捕捉人类行为的研究方向一致。我们提供了一个数据增强算法、一个数据集以及一个机器学习模型来支持此任务。这项工作填补了学习有向无环图中差异的空白，并为更好的比较可视化做出了贡献。