Random Graph Generation in Hyperedge Replacement Languages
Bibliographic record
Abstract
We present a novel approach for the random generation of graphs in context-free hypergraph languages. It is obtained by adapting both of Mairson's generation algorithms for context-free string grammars to the setting of hyperedge replacement grammars. It provides a concrete instrument for the generation of unbiased graph data where a solid mathematical proof is required for the validation of procedures, filling an important gap in the field of random testing. Our main result is that for non-ambiguous hyperedge replacement grammars, the method is guaranteed to efficiently generate hypergraphs uniformly at random in user-specified domains. It means that testing for the sought properties in the generated graph is no longer required since they are directly inferred by the grammar. The efficiency of the method is ensured by the proofs of polynomial time and space asymptotic behaviors. Our secondary result is that it greatly extends the range of either context-free and non-context-free string languages to sample from with a uniform distribution through the use of string graph grammars. We prove how ambiguous string grammars can be expressed with equivalent non-ambiguous hyperedge replacement grammars, overcoming the current limitation for the achievement of the uniformity of the sampling. Our contribution also proposes several case studies of relevant hyperedge replacement languages proving the existence of a representative grammar for a uniform sampling, or, otherwise, their inherent ambiguity by the analysis of particular structures. These languages form a basis for a proof by reduction for more complex languages, greatly improving the possible search for non-ambiguous solutions.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".