{"id":"W7105602842","doi":"10.1109/tsmc.2025.3629990","title":"HyColor: An Efficient Heuristic Algorithm for Graph Coloring","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Graph coloring; Greedy coloring; Heuristic; Greedy algorithm; Graph; Fractional coloring; Efficient algorithm; Edge coloring","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004881787,0.0008580235,0.001535111,0.00190172,0.002397203,0.002609073,0.0009995766,0.0006858429,0.00002488041],"category_scores_gemma":[0.0001224484,0.0008085019,0.0005446396,0.00226645,0.0005066994,0.0002222672,0.00001136871,0.0007044674,0.0001536929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000313635,"about_ca_system_score_gemma":0.0004639298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158564,"about_ca_topic_score_gemma":0.00008570532,"domain_scores_codex":[0.9915322,0.001050854,0.002625239,0.001983825,0.00152283,0.001285034],"domain_scores_gemma":[0.9929988,0.002838835,0.0006890799,0.001621627,0.001128105,0.0007235476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002416802,0.001774348,0.00004808483,0.0008195799,0.0009139638,0.00002030129,0.001668382,0.9270467,0.0002296109,0.01238263,0.001371425,0.05348328],"study_design_scores_gemma":[0.002243464,0.0009427107,0.00007559743,0.001367801,0.0007904738,0.00008237604,0.007448175,0.9691018,0.0002535468,0.0003037447,0.01657987,0.0008104593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02671664,0.007002946,0.9328948,0.0001408293,0.02726425,0.003388402,0.0008167288,0.0002471902,0.001528222],"genre_scores_gemma":[0.9722894,0.0003446858,0.001560808,0.00006354618,0.0003851689,0.0009075266,0.000008729398,0.0000801492,0.02435996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9455728,"threshold_uncertainty_score":0.9994366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05214064452764151,"score_gpt":0.331059496022247,"score_spread":0.2789188514946055,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}