Parallel social network mining for interesting ‘following’ patterns
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
Summary Social networking sites (e.g., Facebook, Google+, and Twitter) have become popular for sharing valuable knowledge and information among social entities (e.g., individual users and organizations), who are often linked by some interdependency such as friendship. As social networking sites keep growing, there are situations in which a user wants to find those frequently followed groups of social entities so that he can follow the same groups. In this article, we present (i) a space‐efficient bitwise data structure for capturing interdependency among social entities; (ii) a time‐efficient data mining algorithm that makes the best use of our proposed data structure for serial discovery of groups of frequently followed social entities; and (iii) another time‐efficient data mining algorithm for concurrent computation and discovery of groups of frequently followed social entities in parallel so as to handle high volumes of social network data. Evaluation results show the efficiency and practicality of our data structure and social network data mining algorithms. Copyright © 2016 John Wiley & Sons, Ltd.
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 | 0.001 |
| 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