Overlapping Community Detection to Generate a Maximum Proximity-Driven Feedback Method for Group Consensus Under Social Network
Bibliographic record
Abstract
With the rise of social network group decision-making (SN-GDM), research on trust relationships has aided in achieving group consensus. However, as network structures grow more complex, there is a growing focus on studying overlapping communities. Most existing methods do not take into account the overlap between communities, thereby not fully revealing the interactive consensus within groups where many users are involved. Additionally, the role of overlapping nodes, which belong to multiple communities, in information exchange and promoting cooperation deserves further investigation. To address these issues, this research provides a maximum proximity-based feedback mechanism, utilizing overlapping structures, which offers effective and acceptable recommendations to guide subgroup interactions. First, higher order structural importance-based method (HoSIM) is used to detect overlapping communities, followed by the concept of proximity degree (PD) for the first time to identify high-quality overlapping nodes within them. Second, a feedback mechanism driven by overlapping communities based on proximity is proposed. By using PDs as weights, it participates in the formation of recommendations and the determination of interaction willingness, thereby improving the group consensus to the desired level. Finally, through numerical experiments and comparative analysis, the superiority of this study is demonstrated not only in consensus rounds but also in adjusting costs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".