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Record W4391361420 · doi:10.18280/mmep.110112

Dominance Parameters in Prism Graphs: A Comparative Study of Minimum Dominating Sets

2024· article· en· W4391361420 on OpenAlexvenueno aff
Arasu Rajagopal, Parvathi Narayan

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Graph Theory Research
Canadian institutionsnot available
Fundersnot available
KeywordsDominance (genetics)PrismMathematicsCombinatoricsComputer sciencePhysicsBiologyOpticsGenetics

Abstract

fetched live from OpenAlex

Let G=(V, E) be a graph.A dominating set S of graph G is defined as a set of vertices such that every vertex in V\S is adjacent to at least one vertex in S. The domination number of graph G, denoted as γ(G), corresponds to the size of the smallest dominating set within G.In other words, γ(G) represents the number of vertices required in the minimum dominating set to cover all other vertices in the graph G.In the graph G, our objective is to position a protector at each vertex within a subset S of V, ensuring that S forms a dominating set, effectively covering all other vertices in G.Moreover, in the event that a protector positioned at vertex needs to move along an edge to protect an unguarded vertex u, the arrangement of protectors should maintain the property of forming a dominating set for the graph.In other words, the movement of protectors should maintain the property of domination within the graph, ensuring efficient coverage and defense across the network.The bare minimum of security guards is necessary to protect all vertices in the graphs.In this article, we find the bounds for domination, independent domination number (IDN), connected domination number(CDN), total domination number(TDN), and the secure domination number(SDN) denoted byγ(A n ), γ i (A n ), γ c (A n ), γ t and γ s (A n ) respectively for the antiprism graph, where A n denoted the 4 -regular graph with girth 3. We further establish that the TDN is greater than or equal to the SDN of the antiprism graph for ≥ 3.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.381
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.293
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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