Theoretical analysis for tree-like networks using random geometry
Bibliographic record
Abstract
Among various network topologies, tree-like networks, also known as hierarchical networks are proposed to decrease the overhead of the routing table especially for the situation involving many network nodes. Usually, the routing table size and the routing complexity are the two crucial concerns in designing a large network. Although there have been various algorithms to optimise the routing strategies for the hierarchical networks, hardly exists any work in studying and evaluating the routing table size and the routing complexity rigorously in the statistical sense. In this study, the authors generalise a new mathematical framework by applying the point process in random geometry. The new framework proposed by the authors leads to the explicit statistical measures of the routing table size and the routing complexity, which can be specified as the functions of the hierarchical network parameters including the number of the hierarchical levels and the cluster population for each hierarchical level. After the relationship between the network topology and these two network performance measures (routing complexity and routing table size) is established, a cluster-population optimisation method for hierarchical networks is presented. The simulation results are also provided to demonstrate the advantage of a hierarchical network over the associated conventional network without hierarchy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".