Automated generation of consistent, diverse and structurally realistic graph models
Bibliographic record
Abstract
Abstract In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".