Graphs whose \(l_p\)-optimal rankings are \(l_{\infty}\) Optimal
Bibliographic record
Abstract
A ranking on a graph G is a function f : V ( G ) → { 1 , 2 , … , k } with the following restriction: if f ( u ) = f ( v ) for any u , v ∈ V ( G ) , then on every u v path in G , there exists a vertex w with f ( w ) > f ( u ) . The optimality of a ranking is conventionally measured in terms of the l ∞ norm of the sequence of labels produced by the ranking. In \cite{jacob2017lp} we compared this conventional notion of optimality with the l p norm of the sequence of labels in the ranking for any p ∈ [ 0 , ∞ ) , showing that for any non-negative integer c and any non-negative real number p , we can find a graph such that the sets of l p -optimal and l ∞ -optimal rankings are disjoint. In this paper we identify some graphs whose set of l p -optimal rankings and set of l ∞ -optimal rankings overlap. In particular, we establish that for paths and cycles, if p > 0 then l p optimality implies l ∞ optimality but not the other way around, while for any complete multipartite graph, l p optimality and l ∞ optimality are equivalent.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".