Correlation Analysis between Maximal Clique Size and Centrality Metrics for Random Networks and Scale-Free Networks
Bibliographic record
Abstract
The high-level contribution of this paper is a comprehensive analysis of the correlation levels between node centrality (a computationally light-weight metric) and maximal clique size (a computationally hard metric) in random network and scale-free network graphs generated respectively from the well-known Erdos-Renyi (ER) and Barabasi-Albert (BA) models. We use three well-known measures for evaluating the level of correlation: Product-moment based Pearson's correlation coefficient, Rank-based Spearman's correlation coefficient and Concordance-based Kendall's correlation coefficient. For each of the several variants of the theoretical graphs generated from the ER and BA models, we compute the above three correlation coefficient values between the maximal clique size for a node (maximum size of the clique the node is part of) and each of the four prominent node centrality metrics (degree, eigenvector, betweenness and closeness). We also explore the impact of the operating parameters of the theoretical models for generating random networks and scale-free networks on the correlation between maximal clique size and the centrality metrics.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".