Survey of Graph Neural Network Methods for Dynamic Link Prediction
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
Graph Neural Network (GNN) methods for Dynamic Link Prediction (DLP) have been a very active research area in recent years. DLP extends traditional LP to time-varying graphs that demand models incorporating structural and temporal features. This survey studies the application of GNN methods for DLP in evolving networks. The paper offers a comprehensive overview of GNN-based methods, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE. We analyze these methods by comparing their scalability, ability to handle noise and incomplete data, and their effectiveness in modeling heterogeneous and dynamic graphs. Despite advancements, no single model effectively addresses all key challenges simultaneously, particularly in handling large-scale dynamic graphs, mitigating data sparsity, and capturing long-term temporal dependencies. This gap highlights the need for further research to develop specific methods for DLP. Finally, we explore some open and ongoing research directions for future work.
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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 | 0.001 |
| 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