Graphs with constant balancing number
Bibliographic record
Abstract
In this paper, we study the existence of unavoidable 2-edge-colored patterns in edge-colorings of the complete graph. We are interested in how these patterns change as the densities of the color classes change. A graph is called balanceable if it can be found, with half its edges in one color and half of them in the other, in any 2-edge-coloring of Kn with sufficiently many edges in each color class and n large enough. The balancing number bal(n,G) of a balanceable graph G is the maximum number m of edges such that there is a coloring of Kn with m edges in one color class without having a balanced copy of G. Equivalently, any 2-edge-coloring of Kn with more than bal(n,G) edges in each color contains a balanced copy of G. Graphs with constant (not depending on n) balancing number have been previously characterized. We give a new proof of such characterization that allows us not only to understand in a deeper way the structure of the graphs with constant balancing number but also to show that bal(n,G) is quadratic on the number of edges of G, a bound that differs substantially from the previous known that was exponential.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".