MétaCan
Menu
Back to cohort
Record W2789506679 · doi:10.82308/145

Layered graph drawing

2005· article· en· W2789506679 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOpen MIND · 2005
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsMcGill University
Fundersnot available
KeywordsParameterized complexityGraph drawingPlanar graphComputer scienceBounded functionGraphAlgorithmMathematicsTheoretical computer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.041
GPT teacher head0.306
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it