Research on Optimization of National Security Education Knowledge Dissemination Path Based on Graph Theory Algorithm
Bibliographic record
Abstract
National security education in the new era puts forward new and higher expectations on the scope, degree, speed and object of knowledge dissemination, while presenting new dissemination characteristics such as all-media and group emergence.Based on graph theory algorithm, this study proposes a dissemination model with credibility constraints about national security education knowledge.Text mining is used to crawl out the discussions of social network users on national security education knowledge in Sina Weibo and Baidu search, and the dissemination mechanism of national security knowledge is explored through text analysis.Based on this, different expectations of information dissemination are set to conduct numerical simulation.The simulation results show that the model is very sensitive to the changes of parameters, in the case of 0 1 R , with the increase of , the time for S to reach the steady state decreases, and the time for I to reach the great value decreases, while the great value increases, when 0.03 , Max 39.86 I ; when 0.3 , Max 37.23 I , the model plays an important role in controlling and managing the knowledge dissemination.The knowledge diffusion model based on graph theory algorithm proposed in this paper can achieve an average knowledge stock of 0.924 under regular networks and only 0.726 under scale-free networks.In terms of knowledge diffusion rate, this model has the optimal knowledge diffusion compared to the existing traditional knowledge diffusion model and random diffusion model.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".