Graph Compression with a Genetic Algorithm: Exploring Fitness, Randomness, and Efficiency
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".