Graph Compression with a Genetic Algorithm: Exploring Fitness, Randomness, and Efficiency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Analysis of the enormous quantities of data stored in graphs is difficult due to size and complexity. One possible solution to address these issues is graph compression, reducing the size of the graph and providing a summary of the data contained within it. We use a genetic algorithm to merge nodes in the graph, with a fitness function designed to minimize distortion from the compression. Three methods to select the nodes to be merged are compared: two previously-studied methods and one new method that introduces increased randomization in a local search. The differences in results obtained by the methods are thoroughly analyzed as they are applied to a set of eight graphs from various domains. The increased randomization within a local search, initially expected to improve runtime at a possible cost of decreased fitness, somewhat surprisingly obtains the best results for several experiments, which is believed to be due to better exploration. Counterintuitively, it also has higher runtime on some experiments, believed to be a feature of the amount of time spent evaluating poor solutions in conjunction with the fact that solutions are less frequently available in the lookup table.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it