Dynamic Network Edge Analysis for Internet of Vehicles with Graph Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Internet of Vehicles is a true enabler of in-telligent transportation, but it also faces communication and connectivity challenges due to the heterogeneity and high mobility of vehicles. More reliably, defining the vehicular network edge substantially helps cope with the highly dynamic environment of vehicles. Previous approaches have employed intelligence, prediction, optimization, and incentive modelling strategies to reduce communication challenges. These approaches still experience flexibility, scalability, and latency challenges, reducing service quality. Our work integrates Graph Neural Networks (GNNs) and clustering methodologies to define and maintain the vehicular edge. We propose an iterative GNN-based vehicular edge clustering framework with three different iterative learning procedures to facilitate the extraction of intricate temporal, spatial and functional patterns within vehicular networks. The experiments demonstrate that the proposed approach provides a promising solution to incorporate insights from trending communication and mobility features and improve the responsiveness of sporadic connectivity in a dynamic environment.
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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 | 0.001 |
| 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