Packing paths in digraphs
Bibliographic record
Abstract
Abstract Let ${\cal G}$ be a fixed set of digraphs. Given a digraph H , a ${\cal G}$ ‐packing in H is a collection ${\cal P}$ of vertex disjoint subgraphs of H , each isomorphic to a member of ${\cal G}$ . A ${\cal G}$ ‐packing ${\cal P}$ is maximum if the number of vertices belonging to members of ${\cal P}$ is maximum, over all ${\cal G}$ ‐packings. The analogous problem for undirected graphs has been extensively studied in the literature. The purpose of this paper is to initiate the study of digraph packing problems. We focus on the case when ${\cal G}$ is a family of directed paths. We show that unless ${\cal G}$ is (essentially) either $\{ \vec {P}_1 \}$ , or $\{ \vec {P}_1, \vec {P}_2 \}$ , the G ‐packing problem is NP‐complete. When ${\cal G} = \{ \vec {P}_1 \}$ , the ${\cal G}$ ‐packing problem is simply the matching problem. We treat in detail the one remaining case, ${\cal G} = \{ \vec {P}_1, \vec {P}_2 \}$ . We give in this case a polynomial algorithm for the packing problem. We also give the following positive results: a Berge type augmenting configuration theorem, a min‐max characterization, and a reduction to bipartite matching. These results apply also to packings by the family ${\cal G}$ consisting of all directed paths and cycles. We also explore weighted variants of the problem and include a polyhedral analysis. © 2003 Wiley Periodicals, Inc. J Graph Theory 44: 81–94, 2003
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".