Self-Stabilizing Algorithms for Computing Maximal Distance-2 Independent Sets and Minimal Dominating Sets in Networks
Bibliographic record
Abstract
This study devises self-stabilizing algorithms that leverage the expression distance-2 paradigm to compute (i) a maximal distance-2 independent set, wherein selected nodes maintain a separation exceeding two edges, ensuring non-adjacency; and (ii) a minimal dominating set, wherein each external node has at least one node as a neighbor in the dominating set.The efficacy and convergence of the algorithms are established through rigorous proofs within the framework of the expression model.Extensive simulation tests validate the algorithms' proficiency in selecting a minimal subset of nodes across expansive network topologies.These algorithms find practical applications in network operations, particularly in ad hoc and wireless sensor networks for the selection of cluster heads that facilitate critical services.Moreover, the self-stabilizing property of the algorithms guarantees the robust reconfiguration of cluster heads post-failure, thereby preserving network functionality amidst disruptions.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".