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Record W4408986240 · doi:10.1007/s00493-025-00147-1

Induced Subgraphs of $$K_r$$-Free Graphs and the Erdős–Rogers Problem

2025· article· lv· W4408986240 on OpenAlexaff
Lior Gishboliner, Oliver Janzer, Benny Sudakov

Bibliographic record

VenueCOMBINATORICA · 2025
Typearticle
Languagelv
FieldMathematics
TopicLimits and Structures in Graph Theory
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMathematicsCombinatoricsDiscrete mathematics

Abstract

fetched live from OpenAlex

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

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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