Generating random partial correlation matrices with an application to redundant variables and bridge centrality
Bibliographic record
Abstract
The Gaussian graphical model (GGM) estimates partial correlations among variables and is a popular network model for psychological symptom data. Centrality indices are functions of partial correlations and are commonly used to interpret the importance of variables in fitted networks. For instance, bridge centrality quantifies the extent to which a variable (or symptom) has connections with variables in other communities (or symptom clusters) and may aid in understanding comorbidity of mental disorders. While critiques have emerged concerning the stability of centrality indices under changes to network composition, methods to simulate and study networks under some conditions of interest are underdeveloped. For example, extant approaches do not easily accommodate custom range restrictions on individual matrix elements or constraints on the pattern of partial correlations across the matrix, which limits researcher control over the data space and makes bridge centrality difficult to study. We adapted a Markov Chain Monte Carlo (MCMC) approach to generate random partial correlation matrices from a space constrained based on user-imposed specifications. We examined our MCMC method with two clusters of variables in which one variable had high bridge centrality but was highly correlated with another variable. We fit GGMs to the random partial correlation matrices and evaluated changes in bridge strength under removal of one of these variables. MCMC had similar coverage of the intended data space compared to a uniform sampling approach, but was faster to generate acceptable matrices. Bridge strength showed good stability and generally changed in an interpretable direction when one variable was removed.
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.
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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".