Test problems and representations for graph evolution
Bibliographic record
Abstract
Graph evolution - evolving a graph or network to fit specific criteria - is a recent enterprise because of the difficulty of representing a graph in an easily evolvable form. Simple, obvious representations such as adjacency matrices can prove to be very hard to evolve and some easy-to-evolve representations place severe limits on the space of graphs that is explored. This study fills in a gap in the literature by presenting two scalable families of benchmark functions. These functions are tested on a number of representations. The first family of benchmark functions is matching the eccentricity sequences of graphs, the second is locating graphs that are relatively easy to color non-optimally. One hundred examples of the eccentricity sequence matching problem are tested. The examples have a difficulty, measured in time to solution, that varies through four orders of magnitude, demonstrating that this test problem exhibits scalability even within a particular size of problem. The ordering by problem hardness, for different representations, varies significantly from representation to representation. For the difficult coloring problem, a parameter study is presented demonstrating that the problem exhibits very different results for different algorithm parameters, demonstrating its effectiveness as a benchmark problem.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".