Monitoring dynamic networks: A simulation‐based strategy for comparing monitoring methods and a comparative study
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
Abstract Recently, there has been a lot of interest in monitoring and identifying changes in dynamic networks, which has led to the development of a variety of monitoring methods. New methods are often designed for a specialized use‐case and rarely compared to competing methods in a systematic fashion. In light of this, the use of simulation is proposed to compare the performance of network monitoring methods over a variety of dynamic network changes. Using the family of simulated dynamic networks, the performance of several state‐of‐the‐art social network monitoring methods from the literature are compared. Their performance over a variety of types of change is compared; both increases in communication levels as well as changes in community structure are considered. It is shown that there does not exist one method that is uniformly superior to the others; the best method depends on the context and the type of change one wishes to detect. As such, it is concluded that a variety of methods are needed for network monitoring and that it is important to understand in which scenarios a given method is appropriate.
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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 | 0.001 | 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