k-robust network design using resistance distance: Case of RocketFuel and power grids
Bibliographic record
Abstract
This paper reconsiders the problem of robust network design form a different point of view using the concept of resistance distance from network science. It has been shown that some important network performance metrics, such as average utilization in a communication network or total power dissipation in an electrical grid, can be expressed in terms of linear combination of point-to-point resistance distances of a graph. In this paper we choose to have a weighted linear combination of resistance distances, referred to as weighted network criticality (WNC), as the objective and we investigate the vulnerability of different network types. In particular, we formulate a min-max convex optimization problem to design k-robust networks and we provide extension to account for joint optimization of resources and flows. We study the solution of the optimization problem in two different networks. First we consider RocketFuel topologies and Abilene as representatives for service provider networks, and we show gains that can be achieved by optimizing link capacities and flows in RocketFuel topologies and Abilene. In the second experience, we show the application of the proposed optimization problem in designing robust electrical grids.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".