A study on population adaptation in social networks based on knowledge migration in cultural algorithm
Bibliographic record
Abstract
Networks can be analyzed from different aspects-micro and macro. If we assume that the main asset of each network is its population and the key difference between populations is their knowledge, then it is knowledge that drives the evolution of any network. In this paper, the behavior and status of a network will be analyzed in a case where a population from one network migrates to another similar network and transfers its knowledge to it. In fact, we are going to find how a migrated population will adapt itself to a new environment with similar characteristics based on the knowledge that it has learned from the previous network and what is the role of this prior knowledge in its evolution. For this purpose, different scenarios are modeled by employing a cultural algorithm with various networks and populations on two different cases: a population with migrated knowledge and a population without it. The results clearly show that when the changes in the structure of networks are less than 25%, trained population can adapt itself with the new network very fast but when the difference is higher, in the best case they perform like a random population without any training.
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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 | 0.014 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".