Training and Evaluating Graph Generative Models
Bibliographic record
Abstract
In this thesis-by-articles we make several contributions related to graph generative models (GGMs) and their applications. \n \nIn our first article, we investigate the use of GGMs for the sequential design of 3D structures. We propose LEGO as a toy problem that is complex enough to approximate real-world design and develop a method for representing simple LEGO structures as a graph to train a GGM. We extend a popular GGM approach, Deep Generative Models of Graphs (DGMG), to operate on these LEGO structures and propose several evaluation metrics originally developed for generative models of images that utilize a relevant pretrained network. \n \nIn our second article, we dive deeper into the proposed metrics to identify a single metric that can be used to evaluate GGMs regardless of domain. We propose replacing the pretrained network used in evaluation with a randomly initialized one to reduce overhead requirements, and design several experiments to objectively score each metric on several criteria. Through this process, we show that pretraining the network is not required: a network with random parameters is sufficient for evaluation. We further identify several strong metrics that can be used to easily evaluate GGMs across domains.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".