Betweenness-based Ranking of Edges using the Principal Components of the Complements of Local Clustering Coefficient and Neighborhood Overlap
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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