Induced Subgraphs of $$K_r$$-Free Graphs and the Erdős–Rogers Problem
Bibliographic record
Abstract
Abstract For two graphs F, H and a positive integer n, the function $$f_{F,H}(n)$$ f F , H ( n ) denotes the largest m such that every H-free graph on n vertices contains an F-free induced subgraph on m vertices. This function has been extensively studied in the last 60 years when F and H are cliques and became known as the Erdős–Rogers function. Recently, Balogh, Chen and Luo, and Mubayi and Verstraëte initiated the systematic study of this function in the case where F is a general graph. Answering, in a strong form, a question of Mubayi and Verstraëte, we prove that for every positive integer r and every $$K_{r-1}$$ K r - 1 -free graph F, there exists some $$\varepsilon _F>0$$ ε F > 0 such that $$f_{F,K_r}(n)=O(n^{1/2-\varepsilon _F})$$ f F , K r ( n ) = O ( n 1 / 2 - ε F ) . This result is tight in two ways. Firstly, it is no longer true if F contains $$K_{r-1}$$ K r - 1 as a subgraph. Secondly, we show that for all $$r\ge 4$$ r ≥ 4 and $$\varepsilon >0$$ ε > 0 , there exists a $$K_{r-1}$$ K r - 1 -free graph F for which $$f_{F,K_r}(n)=\Omega (n^{1/2-\varepsilon })$$ f F , K r ( n ) = Ω ( n 1 / 2 - ε ) . Along the way of proving this, we show in particular that for every graph F with minimum degree t, we have $$f_{F,K_4}(n)=\Omega (n^{1/2-6/\sqrt{t}})$$ f F , K 4 ( n ) = Ω ( n 1
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".