Graph-Tensor Neural Networks for Network Traffic Data Imputation
Why this work is in the frame
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Bibliographic record
Abstract
It is important to estimate the global network traffic data from partial traffic measurements for many network management tasks, including status monitoring and fault detection. However, existing estimation approaches cannot well handle the topological correlations hidden in network traffic and suffer from limited imputation performance. This paper proposes a deep learning approach for network traffic imputation, which well exploits the topological structure of network traffic. We first model the network traffic as a novel graph-tensor and derive a theoretical recovery guarantee. Then we develop an iterative graph-tensor completion algorithm and propose a graph neural network for network traffic imputation by unfolding the iterative algorithm. The proposed graph neural network well captures the topological correlations of network traffic and achieves accurate imputation. Extensive experiments on real-world datasets show that the proposed graph neural network achieves about one-half lower relative square error while at least ten times faster imputation speed than the existing methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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