Hypergraphs, existential closure, and related problems
Bibliographic record
Abstract
In this thesis, we present results from multiple projects with the theme of extending results from graphs to hypergraphs. We first discuss the existential closure property in graphs, a property that is known to hold for most graphs but in practice, examples of these graphs are hard to find. Specifically, we focus on finding necessary conditions for the existence of existentially closed line graphs and line graphs of hypergraphs. We then present constructions for generating infinite families of existentially closed line graphs. Interestingly, when restricting ourselves to existentially closed planar line graphs, we find that there are only finitely many such graphs. Next, we consider the notion of an existentially closed hypergraph, a novel concept that retains many of the necessary properties of an existentially closed graph. Again, we present constructions for generating infinitely many existentially closed hypergraphs. These constructions use combinatorial designs as the key ingredients, adding to the expansive list of applications of combinatorial designs. Finally, we extend a classical result of Mader concerning the edge-connectivity of vertextransitive graphs to linear uniform vertex-transitive hypergraphs. Additionally, we show that if either the linear or uniform properties are absent, then we can generate infinite families of vertex-transitive hypergraphs that do not satisfy the conclusion of the generalised theorem.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.003 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".