A Self Fixing Intelligent Ant Clustering Algorithm For Graphs
Bibliographic record
Abstract
In this paper, we introduce two ant based algorithms for the graph clustering problem. The first algorithm, Intelligent Ant Clustering (IAC), uses techniques such as hopping ants, relaxed drop function, ants with memories, and stagnation control as improvements to the original ant graph clustering algorithm AC-KLS by Kuntz et al. [1]. The second algorithm, Self Fixing Intelligent Ant Clustering (SFIAC), is inspired by polymorphic ant species such as the Pheidole genus [2]. In SFIAC, a second type of major ants (the foragers) is introduced to improve the global clustering quality in addition to the minor workers (the housekeepers) that run IAC locally. SFIAC outperforms or achieves the same modularity values as ACO-MMAS [3] and IAC on 7 out of 10 benchmark networks and is robust against different graphs. In practice, the speed of SFIAC is at least 10 times faster than MMAS, making it a comparatively scalable algorithm.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".