Are We in Agreement? Benchmarking and Reliability Issues between Social Network Analytic Programs
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.330 | 0.754 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".