Routing-Oblivious Network Tomography with Flow-Based Generative Model
Bibliographic record
Abstract
Given the high cost associated with directly measuring the traffic matrix (TM), researchers have dedicated decades to devising methods for estimating the complete TM from low-cost link loads by solving a set of heavily ill-posed linear equations. Today’s increasingly intricate networks present an even greater challenge: the routing matrix within these equations can no longer be deemed reliable. To address this challenge, we, for the first time, employ a flow-based generative model for TM estimation by establishing an invertible correlation between TM and link loads, oblivious of the routing matrix. We demonstrate that the lost information within the ill-posed equations can be independently segregated from the TM. Our model collaboratively learns the invertible correlations between TM and link loads as well as the distribution of the lost information. As a result, our model can unbiasedly reverse-transform the link loads to the true TM. Our model has undergone extensive experiments on two real-world datasets. Surprisingly, even without knowledge of the routing matrix, it significantly outperforms six representative baselines in deterministic and noisy routing scenarios regarding estimation accuracy and distribution similarity. Particularly, if the actual routing matrix is absent, our model can improve the performance of the best baseline by 41% ∼ 58%.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".