Sampling online social networks by random walk
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
This paper proposes to use simple random walk, a sampling method supported by most online social networks (OSN), to estimate a variety of properties of large OSNs. We show that due to the scale-free nature of OSNs the estimators derived from random walk sampling scheme are much better than uniform random sampling, even when uniform random samples are available disregarding the notorious high cost of obtaining the random samples. The paper first proposes to use harmonic mean to estimate the average degree of OSNs. The accurate estimation of the average degree leads to the discovery of other properties, such as the population size, the heterogeneity of the degrees, the number of friends of friends, the threshold value for messages to reach a large component, and Gini coefficient of the population. The method is validated in complete Twitter data dated in 2009 that contains 42 million nodes and 1.5 billion edges.
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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) | 0.002 | 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