Modelling Influence in a Social Network: Metrics and Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social recommender systems are a recently introduced type of decision support system. One of the issues to be resolved in social recommender systems is the identification of opinion leaders in a network. The focus of this paper is the analysis of a network based on the interactions between users called behavioral analysis. The hypothesis explored in this paper is that Influence Rank can be quantified based on the interaction between users and their behavior. The Influence Rank for a node is defined as the average Influence Rank of its neighborhoods combined with another index called Magnitude of Influence. The correlation between the proposed indices is analyzed in this paper. This combined measure is calculated by a recursive algorithm whose calculation complexity is non-polynomial. However, this measure can be estimated by using the Page Rank algorithm. Results supporting the utility of the measure and the accuracy of its estimation using the Page Rank approximation are presented.
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.
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 | 0.000 | 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 it