Enumerating Minimum Feedback Vertex Sets in directed graphs with union-cat trees
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Bibliographic record
Abstract
The problem of finding a minimum feedback vertex set (MFVS) in a directed graph has been known to be NP-hard for around 40 years: It is one of the problems listed in Karp’s famous 1972 paper. Several strategies to solve the MFVS problem, both exact and approximate, have been proposed. In particular, in 2000, Lin and Jou presented an exact algorithm based on eight graph contraction operators whose complexity is polynomial for a particular class of graphs called DOME-contractible graphs. This paper proposes two contributions. First, we introduce a data structure called union-cat tree that provides, in some cases, a compact representation of a family of constant size subsets of a given finite set. Secondly, we extend Lin and Jou’s algorithm to compute the set of all MFVSs of any directed graph.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it