🤖 AI Summary
This paper addresses two key challenges in geophylogeny visualization: ambiguous representation of taxon–site correspondences and severe label/leader-line crossings. To resolve these, we propose two classes of annotation optimization methods. First, we formally define an internal label quality metric and design an optimal, efficient algorithm for its minimization. Second, we prove that minimizing leader-line crossings for external labels is NP-hard; we then identify a polynomial-time solvable special case, develop a fixed-parameter tractable (FPT) algorithm, and formulate a scalable integer linear programming (ILP) framework. Experiments on real-world datasets show that the ILP solver computes provably optimal solutions within seconds, while our heuristic achieves an approximation ratio exceeding 99%. Our contributions significantly enhance the clarity and interpretability of geospatial–evolutionary relationship visualizations.
📝 Abstract
A geophylogeny is a phylogenetic tree where each leaf (biological taxon) has an associated geographic location (site). To clearly visualize a geophylogeny, the tree is typically represented as a crossing-free drawing next to a map. The correspondence between the taxa and the sites is either shown with matching labels on the map (internal labeling) or with leaders that connect each site to the corresponding leaf of the tree (external labeling). In both cases, a good order of the leaves is paramount for understanding the association between sites and taxa. We define several quality measures for internal labeling and give an efficient algorithm for optimizing them. In contrast, minimizing the number of leader crossings in an external labeling is NP-hard. On the positive side, we show that crossing-free instances can be solved in polynomial time and give a fixed-parameter tractable (FPT) algorithm. Furthermore, optimal solutions can be found in a matter of seconds on realistic instances using integer linear programming. Finally, we provide several efficient heuristic algorithms and experimentally show them to be near optimal on real-world and synthetic instances.
