On the importance of local connectivity for Internet topology models
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Bibliographic record
Abstract
(AS) topology generation make structural assumptions about the AS graph. Those assumptions typically stem from beliefs about the true properties of the Internet, e.g. hierarchy and powerlaws, which arise from incorrect interpretations of incomplete observations of the AS topology. In this paper we compare AS topology generation models with several observed AS topologies without making assumptions as to the relative importance of different topological characteristics. We find that although existing AS topology models capture degree-based properties well, they fail to capture the complexity of the local interconnection structure between ASes. We use a wide range of metrics including the weighted spectral distribution and make it available as toolbox 1. We show that the shortcomings of existing models stem from underestimating the complexity of connectivity in the core due to incomplete understanding of collected data limitations, and narrow focus on particular aspects of the AS topology structure. I.
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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 | 0.000 | 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