Method to enhance traffic capacity for two-layer complex networks
Why this work is in the frame
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Bibliographic record
Abstract
The study of traffic dynamics on multilayered networks is a hot issue, where the network topology is composed of two layers of subnetworks, such as wired–wireless networks and peer-to-peer networks. Virtual links on the logical layer can be changed or constructed easily, and therefore the topological structure of the upper logical layer can be efficiently constructed by a link removal strategy. In this paper, edges linking to nodes with large betweenness will be removed. The structure of the upper-layer network can be optimized freely based on our method. Simulation results show that for both shortest path routing strategy and efficient routing strategy, the traffic capacity of two-layer networks is significantly improved. In addition, the average transmission time of packets and the average path length are also investigated in this paper.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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