Uniformly optimal digraphs for strongly connected reliability
Bibliographic record
Abstract
Abstract Boesch et al. conjectured that for any n and m there exists a uniformly optimal (n,m)–graph Gn,m for all terminal reliability, that is, the all‐terminal reliability of Gn,m is at least as large as the all‐terminal reliability of any other graph G with n vertices and m edges, no matter what the probability of an edge being operational is. Although there are counterexamples known when one restricts attention to simple graphs, the conjecture remains open when one allows parallel edges. We consider the analogous problem for strongly connected reliability, that is, the probability that a digraph contains a spanning strongly connected subdigraph, given that each vertex is operational, but arcs are independently operational with probability p. We show that there do indeed exist uniformly optimal digraphs for strongly connected (n,m)–digraphs. We also show that if one restricts attention to simple digraphs (without parallel arcs) then such uniformly optimal digraphs need not exist. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 49(2), 145–151 2007
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".