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
In graph applications (e.g., biological and social networks), various analytics tasks (e.g., clustering and community search) are carried out to extract insight from large and complex graphs. Central to these tasks is the counting of the number of motifs , which are graphs with a few nodes. Recently, researchers have developed several fast motif counting algorithms. Most of these solutions assume that graphs are deterministic, i.e., the graph edges are certain to exist. However, due to measurement and statistical prediction errors, this assumption may not hold, and hence the analysis quality can be affected. To address this issue, we examine how to count motifs on uncertain graphs, whose edges only exist probabilistically. Particularly, we propose a solution framework that can be used by existing deterministic motif counting algorithms. We further propose an approximation algorithm. Extensive experiments on real datasets show that our algorithms are more effective and efficient than existing solutions.
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) | 0.001 | 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