Average-consensus with switched Markovian network links
Bibliographic record
Abstract
Decentralized network estimation of the average initial node value is considered here in a variety of stochastic network settings. Each setting assumes the elements of the network communication graph edge set are modeled as a collection of ergodic Markov chains with slowly switching regime and unknown stationary distribution. In this framework an asymptotic average-consensus is obtained by using a “damped” distributive averaging algorithm in conjunction with an adaptive weighting scheme. The weighting scheme is designed to off-set the unknown probabilities of node communication by associating with each transmission a weight that is inversely proportional to the current estimate of the nodes communication probability. It is shown that for suitably connected graphs with balanced edge sets, and in particular any connected undirected edge set, the weighting scheme and averaging algorithm together yield a network consensus that is bounded to within an arbitrary distance of the average initial node value. The asymptotic node value scaled error measured relative to the node steady-state is also characterized by a vanishing diffusion equation with parameters that approach zero only as the nodes approach consensus. Simulations employ the proposed algorithm to demonstrate its proficiency and illustrate our results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".