Towards Bayesian Learning of the Architecture, Graph and Parameters for Graph Neural Networks
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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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".