Estimation of the Connectivity of Random Graphs Through Q-Learning Techniques
Why this work is in the frame
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Bibliographic record
Abstract
Motivated by its applications to real-world sensor networks, the problem of connectivity estimation of random graphs is investigated. Random graphs are utilized here for representing networks with probabilistic node-to-node communication links. In this context, the unknown probability matrix of the network characterizes the existence of graph edges. This publication presents two novel adaptive algorithms based on the Q-learning technique for estimating said random graphs probability matrix. Those algorithms exploit different methods for computing moving averages. Afterwards, an estimation of the generalized algebraic connectivity is obtained from the estimated probability matrix. The effectiveness of the proposed algorithms is verified by simulation for graphs mimicking underwater sensor networks. Compared to previous work, the authors introduce an estimation scheme tailored to time-varying conditions, a simplification of the upgrade function, and new performance metrics prior to discussing their usefulness. In most scenarios, the proposed procedures outperform the previously proposed approach due to their adaptive nature.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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