  Comparing directed acyclic graphs is essential in various fields such as
healthcare, social media, finance, biology, and marketing. DAGs often result
from contagion processes over networks, including information spreading,
retweet activity, disease transmission, financial crisis propagation, malware
spread, and gene mutations. For instance, in disease spreading, an infected
patient can transmit the disease to contacts, making it crucial to analyze and
predict scenarios. Similarly, in finance, understanding the effects of saving
or not saving specific banks during a crisis is vital. Experts often need to
identify small differences between DAGs, such as changes in a few nodes or
edges. Even the presence or absence of a single edge can be significant.
Visualization plays a crucial role in facilitating these comparisons. However,
standard hierarchical layout algorithms struggle to visualize subtle changes
effectively. The typical hierarchical layout, with the root on top, is
preferred due to its performance in comparison to other layouts. Nevertheless,
these standard algorithms prioritize single-graph aesthetics over comparison
suitability, making it challenging for users to spot changes. To address this
issue, we propose a layout that enhances shape changes in DAGs while minimizing
the impact on aesthetics. Our approach involves outwardly swapping changes,
altering the DAG's shape. We introduce new drawing criteria. Our layout builds
upon a Sugiyama-like hierarchical layout and implements these criteria through
two extensions. We designed it this way to maintain interchangeability and
accommodate future optimizations, such as pseudo-nodes for edge crossing
minimization. In our evaluations, our layout achieves excellent results, with
edge crossing aesthetics averaging around 0.8 (on a scale of 0 to 1).
Additionally, our layout outperforms the base implementation by an average of
60-75\%.
