Modeling the Temporal Dynamics of Social Rating Networks Using Bidirectional Effects of Social Relations and Rating Patterns
Bibliographic record
Abstract
In this paper we first observe and analyze the temporal behavior of users in a social rating network on expressing ratings and creating social relations. Then, we model the temporal dynamics of a SRN based on our observations and using bidirectional effects of ratings and social relationships. While existing models for other types of social networks have captured some of the factors, our model is the first one to represent all four factors, i.e. social relations-on-ratings (social influence), social relations-on-social relations (transitivity), ratings-on-social relations (selection), and ratings-on-ratings (correlational influence). We also model the strength of each effect throughout the evolution of a SRN. Using our model, we develop a generative model for SRNs. Such a model can serve as basis for several purposes, in particular link prediction, rating prediction and prediction of future community structures. Given the sensitive nature of social network data, there are only very few public social rating network datasets. This motivates the development of generative models to create such synthetic datasets for research purposes. Our experimental study on the Epinions dataset demonstrates that the proposed model produces social rating networks that agree with real world data on a comprehensive set of evaluation criteria much better than existing models.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".