RealNet: A Topology Generator Based on Real Internet Topology
Bibliographic record
Abstract
One of the challenges of large-scale network simulations is the lack of scalable and realistic Internet topology generators. Previous topology generators are either not scalable to millions of nodes, or not able to capture characteristics of the Internet topology. In this work, we propose a topology generator which can generate accurate large-scale models of the Internet. We extract the AS (autonomous system) level and router level topology of the Internet with various data sources such as BGP routing tables and traceroute records. With the real Internet topology, we infer the AS topology and the commercial relationship among ASes. We also group the routers into clusters according to their positions in the Internet. A compact routing core is built with the AS topology and router cluster topology. Each generated topology consists of the routing core and a set of end-hosts connected to router clusters. The generated topology is realistic since its routing core is extracted from Internet. We make the assumption of uniform routing policy within an AS. Therefore, the routing path calculation of any source/destination pair consists of finding the AS path for the source/destination ASes and finding the router level path within each AS in the AS path. Since the routing pate depends only on the routing core, its size is independent of the number of end-hosts in the generated topology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".