Betweenness-based Ranking of Edges using the Principal Components of the Complements of Local Clustering Coefficient and Neighborhood Overlap
Bibliographic record
Abstract
Edge betweenness centrality (EBWC) is a computationally-heavy metric used to quantify the contribution of edges for communicating on shortest paths between any two vertices in a network. In this paper, we explore the use of metrics such as the local clustering coefficient (LCC) of a node and the neighborhood overlap (NOVER) scores of the edges as the basis to quantify the contribution of edges for communicating on shortest paths. As vertices with lower LCC and edges with lower NOVER are expected to be unused by their neighbors (and hence unused by any other node in the network as well) and vice-versa for communicating on shortest paths, we propose to develop a principal components analysis (PCA)-based composite betweenness scores for the edges (referred to as PCA_EBW) computed on the basis of a dataset that includes the LCC' (1-LCC) values for the end vertices and the NOVER' (1-NOVER) scores for the edges. When applied over a diverse collection of real-world networks, we notice a moderate-strong Spearman's rank-based correlation between the PCA-EBW scores for the edges and their EBWC values.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".