Are We in Agreement? Benchmarking and Reliability Issues between Social Network Analytic Programs
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 Reliability and validity are key concerns for any researcher. We investigate these concerns as they apply to social network analysis programs. Six well-used and trusted programs were compared on four common centrality measures (degree, betweenness, closeness, and eigenvector) under a variety of network topographies. We identify notable inconsistencies between programs that may not be apparent to the average user of these programs. Specifically, each program may have implemented a variant of a given measure without informing the user of its characteristics. This presents an unnecessary obfuscation for analysts seeking measures that are best suited to the idiosyncrasies of their data, and for those comparing results between programs. Under such a paradigm, the terms in use within the social network analysis community become less precise over time and diverge from the original strength of network analysis: clarity.
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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 | 0.001 |
| 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