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Record W4406270213 · doi:10.1016/j.laa.2025.01.005

Spectral methods for matrix product factorization

2025· article· en· W4406270213 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLinear Algebra and its Applications · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAdvanced Computing and Algorithms
Canadian institutionsCarleton University
FundersUniversity Natural Science Research Project of Anhui ProvinceNational Natural Science Foundation of China
KeywordsMathematicsMatrix decompositionFactorizationMatrix (chemical analysis)Product (mathematics)Algebra over a fieldPure mathematicsCombinatoricsAlgorithmEigenvalues and eigenvectorsGeometry

Abstract

fetched live from OpenAlex

A graph G is factored into graphs H and K via a matrix product if there exist adjacency matrices A , B , and C of G , H , and K , respectively, such that A = B C . In this paper, we study the spectral aspects of the matrix product of graphs, including regularity, bipartiteness, and connectivity. We show that if a graph G is factored into a connected graph H and a graph K with no isolated vertices, then certain properties hold. If H is non-bipartite, then G is connected. If H is bipartite and G is not connected, then K is a regular bipartite graph, and consequently, n is even. Furthermore, we show that trees are not factorizable, which answers a question posed by Maghsoudi et al.

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.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.868
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.022
GPT teacher head0.425
Teacher spread0.403 · 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