Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating patterns
Bibliographic record
Abstract
A social rating network (SRN) is a social network in which edges represent social relationships and users (nodes) express ratings on some of the given items. Such networks play an increasingly important role in reviewing websites such as Epinions.com or online sharing websites like Flickr.com. In this paper, we first observe and analyze the temporal behavior of users in a social rating network, who express ratings and create social relations. Then, we model the temporal dynamics of an SRN based on our observations, using the bidirectional effects of ratings and social relations. While existing models for other types of social networks have captured some of the effects, our model is the first one to represent all four effects, 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). Existing works consider these effects as static and constant throughout the evolution of an SRN, however our observations reveal that these effects are actually dynamic. We propose a probabilistic generative model for SRNs, which models the strength and dynamics of each effect throughout the network evolution. This model can serve for the prediction of future links, ratings or community structures. Due to the sensitive nature of SRNs, another motivation for our work is the generation of synthetic SRN data sets for research purposes. Our experimental studies on two real life datasets (Epinions and Flickr) demonstrate that the proposed model produces social rating networks that agree with real world data on a comprehensive set of evaluation criteria.
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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 | 0.002 |
| 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".