Anti-vertex for neighborhood constraints in subgraph queries
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Bibliographic record
Abstract
This paper focuses on subgraph queries where constraints are present in the neighborhood of the explored subgraphs. We describe anti-vertex, a declarative construct that indicates absence of a vertex, i.e., the resulting subgraph should not have a vertex in its specified neighborhood that matches the anti-vertex. We formalize the semantics of anti-vertex to benefit from automatic reasoning and optimization, and to enable standardized implementation across query languages and runtimes. The semantics are defined for various matching semantics that are commonly employed in subgraph querying (isomorphism, homomorphism, and no-repeated-edge matching) and for the widely adopted property graph model. We illustrate several examples where anti-vertices can be employed to help familiarize with the anti-vertex concept. We further showcase how anti-vertex support can be added in existing graph query languages by developing prototype extensions of Cypher language. Finally, we study how anti-vertices interact with the symmetry breaking technique in subgraph matching frameworks so that their meaning remains consistent with the expected outcome of constrained neighborhoods to connected vertices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it