Novel Complexity Results for Temporal Separators with Deadlines
Bibliographic record
Abstract
We consider two variants, (s,z,𝓁)-Temporal Separator and (s,z,𝓁)-Temporal Cut, respectively, of the vertex separator and the edge cut problem in temporal graphs. The goal is to remove the minimum number of vertices (temporal edges, respectively) in order to delete all the temporal paths that have time travel at most 𝓁 between a source vertex s and target vertex z. First, we solve an open problem in the literature showing that (s,z,𝓁)-Temporal Separator is NP-hard even when the underlying graph has pathwidth bounded by four. We complement this result showing that (s,z,𝓁)-Temporal Separator can be solved in polynomial time for graphs of pathwidth bounded by three. Then we consider the approximability of (s,z,𝓁)-Temporal Separator and we show that it cannot be approximated within factor 2^Ω(log^{1-ε}|V|) for any constant ε > 0, unless NP ⊆ ZPP (V is the vertex set of the input temporal graph) and that the strict version is approximable within factor 𝓁-1 (we show also that it is unliklely that this factor can be improved). Then we consider the (s,z,𝓁)-Temporal Cut problem, we show that it is APX-hard and we present a 2 log₂(2𝓁) approximation algorithm.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".