Computing Storyline Visualizations with Few Block Crossings

TL;DR

This paper introduces a SAT-based method for creating storyline visualizations that minimize block crossings, improving the efficiency and applicability of automated story visualization techniques.

Contribution

It models block crossing minimization as a satisfiability problem, enabling the use of SAT solvers for optimal storyline visualization.

Findings

SAT approach finds optimal solutions efficiently for real-world instances.

Compared with ILP and other algorithms, SAT is more scalable for complex instances.

Different algorithms perform better depending on the instance characteristics.

Abstract

Storyline visualizations show the structure of a story, by depicting the interactions of the characters over time. Each character is represented by an x-monotone curve from left to right, and a meeting is represented by having the curves of the participating characters run close together for some time. There have been various approaches to drawing storyline visualizations in an automated way. In order to keep the visual complexity low, rather than minimizing pairwise crossings of curves, we count block crossings, that is, pairs of intersecting bundles of lines. Partly inspired by the ILP-based approach of Gronemann et al. [GD 2016] for minimizing the number of pairwise crossings, we model the problem as a satisfiability problem (since the straightforward ILP formulation becomes more complicated and harder to solve). Having restricted ourselves to a decision problem, we can apply powerful SAT solvers to find optimal drawings in reasonable time. We compare this SAT-based approach with two exact algorithms for block crossing minimization, using both the benchmark instances of Gronemann et al. and random instances. We show that the SAT approach is suitable for real-world instances and identify cases where the other algorithms are preferable.