Bibliographic record
Abstract
A layered graph drawing is a two-dimensional drawing of a combinatorial graph in which the vertices lie on a given set of horizontal lines. Such drawings are used in application domains such as software engineering, bioinformatics, and VLSI design. In addition to being layered, drawings in these applications may also satisfy other constraints, for example bounds on the number of edge crossings. The problems related to obtaining these drawings are almost always NP -hard, so, in this thesis, we investigate restricted versions of these problems in order to find efficient algorithmic solutions that can be used in practice. As a first very drastic restriction, we consider layered drawings that are planar. Even with this restriction, however, the resulting problems can still be NP -hard. In addition to proving one such hardness result, we do succeed in deriving efficient algorithms for two problems. In both cases, we correct previously published results that claimed extremely simple and efficient algorithmic solutions to these problems. Our solutions, though efficient as well, show that the truth about these problems is significantly more complex than the published results would suggest. We also study non-planar layered drawings, particularly drawings obtained by crossing minimization and minimum planarization. Though the corresponding problems are NP -hard, they become tractable when the value to be minimized is upper-bounded by a constant. This approach to obtaining tractable problems is formalized in a theory called parameterized complexity, and the resulting tractable problems and algorithmic solutions are said to be fixed-parameter tractable ( FPT ). Though relatively new, this theory has attracted a rapidly growing body of theoretical results. Indeed, we derive original FPT algorithms with the best-known asymptotic running times for planarization in two layer drawings. Because parameterized complexity is so new, little is known about its implications to the practice of graph drawing. Consequently, we have implemented a few FPT algorithms and compared them experimentally with previously implemented approaches, especially integer linear programming (ILP). Our experiments show that the performance of our FPT planarization algorithms are competitive with current ILP algorithms, but that, for crossing minimization, current ILP algorithms remain the clear winners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".