Core-Periphery Analysis Using Principal Components of the Neighborhood-based Bridge Node Centrality Tuple
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).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".