RZDD: Risk Zone-Diversified Network Design for Disaster Resilience
Bibliographic record
Abstract
With the growing need for a robust network backbone to ensure uninterrupted connectivity in the face of large-scale natural disasters, we introduce the Risk Zone-Diversified Network Design (RZDD) problem. This problem requires diverse paths between source-destination pairs to be risk zone-disjoint, preventing any single disaster from disrupting overall network connectivity. Unlike previous research, we propose an innovative cost framework that considers geographically overlapping links and long-term maintenance costs, providing a comprehensive approach to cost analysis. We prove the intractability of the RZDD problem and present the Risk Zone-Diversified Network Design Algorithm (RZDD-Algorithm). In small-scale networks with a single source-destination pair, our algorithm achieves optimal outcomes. Comparative analysis shows that our method reduces costs by an average of 24% compared to an SRLG algorithm that does not consider the preference of geographically overlapping links. For multiple pairs, our approach maintains a gap ratio within 4% and 7% of optimal solutions. Furthermore, experimental evaluations on large networks demonstrate reductions of 26% and 31% compared to the SRLG baseline for single pairs. We also showcase the efficiency of our method in designing large-scale networks with multiple pairs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".