Optimal edge‐coloring with edge rate constraints
Bibliographic record
Abstract
We consider the problem of covering the edges of a graph by a sequence of matchings subject to the constraint that each edge e appears in at least a given fraction r ( e ) of the matchings. Although it can be determined in polynomial time whether such a sequence of matchings exists or not [Grötschel et al., Combinatorica (1981), 169–197], we show that several questions about the length of the sequence are computationally intractable. Therefore, as is commonly done [Golumbic, Algorithmic graph theory and perfect graphs, 2004], we restrict our investigation to a special class of graphs. In recent work [Birand et al., INFOCOM 2010 Proceedings, 2010], two of the authors dealt with so‐called OLoP ( Overall Local Pooling ) graphs, a class of graphs for which similar matching‐related problems are tractable (namely, in an online distributed wireless network scheduling setting). We therefore focus on these graphs and generalize the results to a larger class of graphs which we call GOLoP graphs. In particular, we show that deciding whether a given GOLoP graph has a matching sequence of length at most k can be done in linear time. In case the answer is affirmative, we show how to construct, in quadratic time, the matching sequence of length at most k . Finally, we prove that, for GOLoP graphs, the length of a shortest sequence does not exceed a constant times the least common denominator of the fractions r ( e ), leading to a pseudopolynomial‐time algorithm for minimizing the length of the sequence. We show that the constant equals 1 for OLoP graphs and, following Seymour [Seymour, Proc. London Math. Soc., 1979], conjecture that the constant is as small as 2 for general graphs. We then show that this conjecture holds for all graphs with at most 10 vertices. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol 62(3), 165–182 2013
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".