Temporal Graph Convolutional Network for Implicit Relation Prediction: Leveraging Timestamps and Confidence
Why this work is in the frame
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Bibliographic record
Abstract
In the dynamic landscape of social network analysis, the accurate prediction of implicit relationships presents a pivotal challenge. This paper introduces an innovative solution, the Relation Temporal Graph Convolutional Network with Confidence (R-CTGCN), specifically designed to address the intricate task of predicting implicit relations within evolving social networks. R-CTGCN unifies timestamp temporal embeddings, confidence metrics, and PFs within a comprehensive graph neural network framework, aiming to capture the evolving dynamics of networks and enhance predictive accuracy. Experimental evaluations conducted on diverse datasets, including Epinions and Enron, showcase R-CTGCN’s superior performance compared to both baseline models and contemporary state-of-the-art methods. The emphasis on the roles of confidence and PFs underscores their significance in implicit relationship prediction. The outcomes contribute substantively to the understanding of predicting implicit relationships, positioning R-CTGCN as a robust tool tailored for complex social network scenarios.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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