Towards Bayesian Learning of the Architecture, Graph and Parameters for Graph Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Real life data often arises from relational structures that are best modeled by graphs. Bayesian learning on graphs has emerged as a framework which allows us to model prior beliefs about network data in a mathematically principled way. The approach provides uncertainty estimates and can perform very well on a small sample size when provided with an informative prior. Much of the work on Bayesian graph neural networks (GNNs) has focused on inferring the structure of the underlying graph and the model weights. Although research effort has been directed towards network architecture search for GNNs, existing strategies are not Bayesian and return a point estimate of the optimal architecture. In this work, we propose a method for principled Bayesian modelling for GNNs that allows for inference of a posterior over the architecture (number of layers, number of active neurons, aggregators, pooling), the graph, and the model parameters. We evaluate our proposed method on three mainstream datasets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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