{"id":"W4416249429","doi":"10.1109/ijcnn64981.2025.11229000","title":"Optimization of Graph Neural Networks Training Using Graph Reordering","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Speedup; Graph; Artificial neural network; Training set; Convergence (economics); Dense graph; Attention network; Deep neural networks; Graph bandwidth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005138746,0.0006292343,0.0008365383,0.001005118,0.0005769143,0.0002766342,0.001448987,0.0003476222,0.00004485905],"category_scores_gemma":[0.00008185136,0.00068225,0.0004668531,0.007590388,0.0004468731,0.001547811,0.0008055484,0.0007419165,3.273925e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006111118,"about_ca_system_score_gemma":0.0001333966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000575592,"about_ca_topic_score_gemma":0.0000222996,"domain_scores_codex":[0.9954083,0.0002859859,0.001438401,0.00123443,0.0004591993,0.001173682],"domain_scores_gemma":[0.9972329,0.000366515,0.0006606458,0.001164945,0.0003701876,0.0002047843],"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":[0.00003728067,0.0000507281,0.0006260363,0.00005427244,0.00008433017,0.0000119401,0.0003187679,0.9052621,0.0001346163,0.01703545,0.00003470528,0.07634982],"study_design_scores_gemma":[0.0006850968,0.0001134302,0.0002303108,0.0004424874,0.00008467969,0.00002032748,0.0001666015,0.9923543,0.0002293799,0.005122502,0.00001959509,0.0005313175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007989971,0.002681246,0.9831104,0.0002961306,0.003491629,0.0005545088,0.000001759703,0.0002347025,0.001639629],"genre_scores_gemma":[0.7464333,0.0004385953,0.2523452,0.0005116058,0.0001107324,0.000007591653,0.00000363458,0.00003482454,0.0001144963],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7384433,"threshold_uncertainty_score":0.9995629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03112615784146965,"score_gpt":0.2749506316482858,"score_spread":0.2438244738068162,"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."}}