On the Conduciveness of Random Network Graphs for Maximal Assortative or Maximal Dissortative Matching
Bibliographic record
Abstract
<p style="margin: 0in 0in 0pt; text-align: justify;"><span style="font-size: 11pt;"><span style="font-family: Times New Roman;">A maximal matching of a graph is the set of edges such that the addition of an edge to this set violates the property of matching (i.e., no two edges of the matching share a vertex). We use the notion of assortative index (ranges from -1 to 1) to evaluate the extent of similarity of the end vertices constituting the edges of a matching. A maximal matching of the edges whose assortative index is as close as possible to 1 is referred to as maximal assortative matching (MAM) and a maximal matching of the edges whose assortative index is as close as possible to -1 is referred to as maximal dissortative matching (MDM). We present algorithms to determine the MAM and MDM of the edges in a network graph. Through extensive simulations, we conclude that random network graphs are more conducive for maximal dissortative matching rather than maximal assortative matching. We observe the assortative index of an MDM on random network graphs to be relatively more closer to the targeted optimal value of -1 compared to the assortative index of an MAM to the targeted optimal value of 1. </span></span></p>
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".