Mining of Diverse Social Entities from Linked Data
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
Nowadays, high volumes of valuable data can be easily generated or collected from various data sources at high velocity. As these data are often related or linked, they form a web of linked data. Examples include semantic web and social web. The social web captures social relationships that link people (i.e., social entities) through the World Wide Web. Due to the popularity of social networking sites, more people have joined and more online social interactions have taken place. With a huge number of social entities (e.g., users or friends in social networks), it becomes important to analyze high volumes of linked data and discover those diverse social entities. In this paper, we present (i) a tree-based mining algorithm called DF-growth, along with (ii) its related data structure called DF-tree, whichallowuserstoe↵ectively and e ciently mine diverse friends from social networks. Results of our experimental evaluation showed both the timeand space-e ciency of our scalable DF-growth algorithm, which makes good use of the DF-tree structure.
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 | 0.002 | 0.001 |
| 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