The Effects of Ant Colony Optimization on Graph Anonymization
Bibliographic record
Abstract
The growing need to address privacy concerns when social network data is released for mining purposes has recently led to considerable interest in various techniques for graph anonymization. These techniques and definitions, although robust are sometimes difficult to achieve for large social net-works. In this paper, we look at applying ant colony opti-mization (ACO) to two known versions of social network anonymization, namely k-label sequence anonymity, known to be NPhard for k ≥ 3. We also apply it to the more recent work of [23] and Label Bag Anonymization. Ants of the artificial colony are able to generate successively shorter tours by using information accumulated in the form of pheromone trails deposited by the edge colonies ant. Computer simu-lations have indicated that ACO are capable of generating good solutions for known harder graph problems. The contributions of this paper are two fold: we look to apply ACO to k-label sequence anonymity and k=label bag based anonymization, and attempt to show the power of ap-plying ACO techniques to social network privacy attempts. Furthermore, we look to build a new novel foundation of study, that although at its preliminary stages, can lead it ground breaking results down the road.
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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 | 0.013 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".