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Record W2058048384 · doi:10.1145/944618.944628

Finding the chromatic number by means of critical graphs

2002· article· en· W2058048384 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

VenueACM Journal of Experimental Algorithmics · 2002
Typearticle
Languageen
FieldDecision Sciences
TopicScheduling and Timetabling Solutions
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCombinatoricsChromatic scaleCritical graphMathematicsGraph coloringGraphDiscrete mathematicsGraph factorizationRandom graphEdge coloringGraph powerLine graph

Abstract

fetched live from OpenAlex

We propose a new exact algorithm for finding the chromatic number of a graph G . The algorithm attempts to determine the smallest possible induced subgraph G' of G which has the same chromatic number as G . Such a subgraph is said critical since all proper induced sub-graph of G' have a chromatic number strictly smaller than G' .The proposed method is particularly helpful when a k -coloring of a non-critical graph is known, and it has to be proved that no ( k -1)-coloring of G exists. Computational experiments on random graphs and on DIMACS benchmark problems demonstate that the new proposed algorithm can solve larger problem than previous known exact methods.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.134
GPT teacher head0.414
Teacher spread0.280 · 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