Bibliographic record
Abstract
In this article we study graphs with inductive neighborhood properties. Let P be a graph property, a graph G = ( V, E ) with n vertices is said to have an inductive neighborhood property with respect to P if there is an ordering of vertices v 1 , …, v n such that the property P holds on the induced subgraph G [ N ( v i )∩ V i ], where N ( v i ) is the neighborhood of v i and V i = { v i , …, v n }. It turns out that if we take P as a graph with maximum independent set size no greater than k , then this definition gives a natural generalization of both chordal graphs and ( k + 1)-claw-free graphs. We refer to such graphs as inductive k -independent graphs. We study properties of such families of graphs, and we show that several natural classes of graphs are inductive k -independent for small k . In particular, any intersection graph of translates of a convex object in a two dimensional plane is an inductive 3 -independent graph; furthermore, any planar graph is an inductive 3 -independent graph. For any fixed constant k , we develop simple, polynomial time approximation algorithms for inductive k -independent graphs with respect to several well-studied NP-complete problems. Our generalized formulation unifies and extends several previously known results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".