Modeling the Clustering Structure of Complex Networks and Calculating Propagation Thresholds for Bandwagon Behavior of Social Media Papers from the Perspective of Knowledge Reproduction and Anti-Regulatory Discipline
Bibliographic record
Abstract
This paper constructs a heterogeneous network adjacency matrix containing multiple user relationships from the connotation of professional organizations and other guides to individual behaviors covered by the take-read mechanism.The GAT algorithm is used to learn the embedding of its heterogeneous network in order to obtain the embedding vectors of user nodes, which serves as the basis for the analysis of the spreading influence of group behavior.An event recognition method based on word embedding and hierarchical cohesive clustering is proposed to analyze the recognition and evolution of social media essay-carrying behavioral events (group behavioral events) for complex networks.We point out that the distribution of group behavior affects the dynamics of information dissemination, set the adoption threshold parameter of the group, and analyze the dissemination pattern of individuals' (individual information) participation in essay-reading behaviors.Analyze the emergence and evolution of thesis-reading behavior in social media, and explore the influence of individual's own attributes and the attitude of neighboring nodes on the evolution of group behavioral events in complex networks.The spreading degree analysis is conducted for different relational social media bandwagon behaviors.When , i j I =0.6 and , i j Ex =0.8, the individual's decision is supported by the neighbor's viewpoints, and the users who have already participated in the paper band-reading activities have a strong attraction to the individual.When the strong degree increases to a certain value, the individual decides to participate in the dissertation banding activity, at which point the individual is no longer influenced by the external environment.The degree of the initial node for the propagation of thesis banding behavior in random networks and small-world networks is linearly and negatively correlated with the percentage of the information audience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".