The Spectrum of Triangle-Free Graphs
Bibliographic record
Abstract
Abstract. Denote by [Formula: see text] the smallest eigenvalue of the signless Laplacian matrix of an [Formula: see text]-vertex graph [Formula: see text]. Brandt conjectured in 1997 that for regular triangle-free graphs [Formula: see text]. We prove a stronger result: If [Formula: see text] is a triangle-free graph, then [Formula: see text]. Brandt’s conjecture is a subproblem of two famous conjectures of Erdős: (1) Sparse-half-conjecture: Every [Formula: see text]-vertex triangle-free graph has a subset of vertices of size [Formula: see text] spanning at most [Formula: see text] edges. (2) Every [Formula: see text]-vertex triangle-free graph can be made bipartite by removing at most [Formula: see text] edges. In our proof we use linear algebraic methods to upper bound [Formula: see text] by the ratio between the number of induced paths with 3 and 4 vertices. We give an upper bound on this ratio via the method of flag algebras.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".