Core-Periphery Analysis Using Principal Components of the Neighborhood-based Bridge Node Centrality Tuple
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
The neighborhood-based bridge node centrality (NBNC) tuple has been proposed in the literature to rank nodes for the extent they could serve as a bridge node. The NBNC tuple of a node v has three entries: (# components in NGv, 1-algebraic connectivity ratio of NGv and degree of node v), where NGv is the neighborhood graph of node v. The research presented in this paper conducts principal component analysis on dataset comprising of NBNC tuples of all the nodes and computes a weighted PC_NBNC score based on the entries for the nodes in the dominating principal components (variances ≥ 1.0). The proposed model is to classify nodes as core (or peripheral) if their weighted PC_NBNC score is ≥ 0.0 (or < 0). The study measures the fractions of core-core, core-peripheral and peripheral-peripheral links and the fractions of core and peripheral nodes and uses these measures to classify a real-world network as either core-heavy or peripheral-heavy. Accordingly, 48 of the 80 real-world networks are classified as core-heavy (observed to be dominated by core nodes and core-core links) and the remaining 32 networks are classified as peripheral-heavy (observed to be dominated by peripheral nodes).
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.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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