An Investigation of Attention Mechanisms in Graph Convolution Networks Applied to Link Prediction Problems
Bibliographic record
Abstract
The link prediction problem is fundamental to many application domains.Recently, deep learning-based models have been proposed to tackle this kind of problem.Graph auto-encoder (GAE) is a framework for unsupervised learning on graph-structured data.By using a graph convolutional network (GCN) encoder and a simple inner product decoder, GAE achieves competitive results in link prediction tasks on citation networks.Another important problem on graph-structured data is node classification.Graph attention mechanism has been shown to have good performance in these tasks.This research investigates whether graph attention mechanisms can achieve good performance in link prediction tasks.We propose the attentive graph auto-encoder (AGAE) model, which incorporates GAE with the graph attention mechanism.The model is compared with GAE on both real-world citation networks and synthetic datasets.Empirical analysis of the AGAE components is included in this research.Investigations on how the model performs on networks with different characteristics is also included.In general, AGAE achieves competitive performance with GAE on citation networks while it outperforms GAE on certain synthetic networks.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".