Managing consensus based on community classification in opinion dynamics
Bibliographic record
Abstract
Opinion dynamics in social networks are fast becoming an essential instrument for concentrating on the effect of individual choices on external public information. One of the main challenges in seeing the dynamics is reaching an opinion consensus acceptable to managers in a social network. This issue is referred to as a consensus-reaching process (CRP). Most studies of CRP focus only on network structure and ignore the effect of agent opinions. In addition, existing methods ignore the diversities between divided communities. How to synthesize individual opinions with community diversities to solve CRP issues has remained unclear. Using the DeGroot model for opinion control, this paper considers the effects of network structures and agent opinions when dividing communities, incorporating community classification and targeted opinion control strategies. First, a community classification enhancement approach is utilized, introducing the concept of ambiguous nodes and their division methods. Second, we separate all communities into three levels, $ Center $, $ Base $, and $ Fringe $, according to the logical regions for opinion control. Third, an edge expansion algorithm and three opinion control strategies are proposed based on the community levels, which can significantly reduce the time it takes for the network to reach a consensus. Finally, numerical analysis and comparison are given to verify the feasibility of the proposed opinion control strategy.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".