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Theoretic Properties of \(k^{th}\) Power Graphs of Finite Groups

2025· article· W4416187190 on OpenAlexvenueno aff
Ojonugwa Ejima, Ahmad Rufai Tasiu, Kazeem Olalekan Aremu

Bibliographic record

VenueInternational Journal of Analysis and Applications · 2025
Typearticle
Language
FieldMathematics
TopicFinite Group Theory Research
Canadian institutionsnot available
Fundersnot available
KeywordsAutomorphismDisjoint setsFinite groupEdge-transitive graphGraphAlgebraic propertiesVertex-transitive graphAlgebraic structureComplement graph

Abstract

fetched live from OpenAlex

In this paper, we investigate the structural and combinatorial properties of the kth power graph Γk(G) associated with a finite group G, where k ≥ 2. The graph Γk(G) is defined by taking the elements of G as vertices and connecting two distinct vertices x and y by an edge if either x = yk or y = xk. This construction generalizes the well-studied power graph of a group and provides new insight into the influence of exponentiation on group elements when viewed through graph-theoretical properties. We show that Γk(G) is a subgraph of the power graph P(G) and analyze conditions under which Γk(G) is connected, disconnected, or empty. Depending on the algebraic structure of G and the arithmetic properties of k, we show that Γk(G) can exhibit a variety of structural forms, including being a tree, a union of disjoint stars, or a complete multipartite graph. For instance, when G = Zn and gcd(k, n) = 1, Γk(G) decomposes into disjoint stars, while for certain non-cyclic groups, the graph becomes multipartite. Additionally, we provide formulas for computing the number of edges in Γk(G) and discuss how subgroup structure and group automorphisms impact the topology of the graph.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.323
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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