Minimum strong diameter of the strong product of complete multipartite graph and path
Bibliographic record
Abstract
Suppose \(G_1=(V_1, E_1)\) is a graph and \(G_2=(V_2, E_2)\) is a strong digraph of \(G_1\), where \(V_1\) and \(V_2\) represent the vertex sets, \(E_1\) and \(E_2\) represent the edge sets. Let \(u\) and \(v\) be any two vertices of \(G_2\). The strong distance \(sd(u,v)\) is the minimum value of edges in a strong subdiagraph of \(G_2\) that contains \(u\) and \(v\). The minimum strong diameter of \(G_2\) is defined as the maximum eccentricity \(se(u)\) from \(u\) to all other vertices in \(G_2\). In this paper, we propose different strong orientation methods to explore the minimum strong diameter of the strong product graph of \(K_{m_1,m_2,\ldots,m_k}\otimes P_n\), where \(K_{m_1,m_2,\ldots,m_k}\) and \(P_n\) represent respectively complete multipartite graph and path. In addition, based on strong orientation methods, a new algorithm is proposed to model the presence or absence of a minimum strong diameter in a strong product graph. Simulation experiments show a trend of simultaneous decrease and concentration in the minimum strong diameter of the strong product graph, as the value of parts in \(K_{m_1,m_2,\ldots,m_k}\) increases while the length of \(P_n\) remains constant.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".