Embeddings of Small Graphs on the Torus
Bibliographic record
Abstract
Embeddings of graphs on the torus are studied. All 2-cell embeddings of the vertex-transitive graphs on 12 vertices or less are constructed. Their automorphism groups and dual maps are also constructed. A table of embeddings is presented. 1. Toroidal Graphs Let G be a 2-connected graph. The vertex and edge sets of G are V (G) and E(G), respectively. E(G) is a multiset consisting of unordered pairs {u,v}, where u,v 2 V (G), and possibly ordered pairs (v,v), as the graphs G will sometimes have multiple edges and/or loops. We write the pair {u,v} as uv, and the ordered pair (v,v) as vv, which represents a loop on vertex v. If u,v 2 V (G) then u ! v means that u is adjacent to v (and so also v ! u). The reader is referred to Bondy and Murty [2], West [11], or Gross and Tucker [3] for other graph-theoretic terminology. An embedding of a graph on a surface is represented combinatorially by a rotation system [3]. This consists of a cyclic ordering of the incident edges, for each vertex v. Let v be a vertex of G, incident on edges e1,e2,...,ek. We write v ! (e1,e2,...,ek) to indicate the cyclic ordering for v in a rotation system. If some ei is a loop vv, then this loop must appear twice in the cyclic adjacency list (e1,e2,...,ek), because walking around the vertex v along a small circle in the torus will require that a loop vv be crossed twice. Thus, we assume that if ei is a loop vv, there is another e 0 in the list corresponding to the same loop vv. Since every loop must appear twice in the rotation system, a loop contributes two to the degree of a vertex. If ei with endpoints uv is not a loop, then it will appear in the cyclic adjacency list of both vertices u and v. Given ei in the list for u, the corresponding ej in the list for v is given by the rotation sytem. Figure 1 shows an embedding of the complete
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".