{"id":"W7114821263","doi":"","title":"Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Graph; Node (physics); Training set; Generalization; Benchmark (surveying); Bridge (graph theory); Feature (linguistics); Feature learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001597084,0.001051132,0.001131263,0.001146551,0.0006260444,0.001157426,0.002544599,0.00196924,0.00340334],"category_scores_gemma":[0.008299707,0.000490061,0.001025751,0.00120929,0.001213836,0.005368961,0.001537831,0.00253723,0.001586588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001424684,"about_ca_system_score_gemma":0.001046646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005991764,"about_ca_topic_score_gemma":0.01180145,"domain_scores_codex":[0.9995322,0.0001489815,0.00001998107,0.0001814883,0.00005639278,0.00006091097],"domain_scores_gemma":[0.9975677,0.001194927,0.0001840735,0.0006484458,0.0002657051,0.000139305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004439592,0.0002820339,0.008992872,0.0003915405,0.0001439946,0.0001602394,0.0002657513,0.506417,0.004542835,0.02973421,0.02720821,0.4214174],"study_design_scores_gemma":[0.0000154247,0.00003888019,0.0002341292,0.00001650129,0.000008535422,0.00002414142,0.0000317264,0.9726259,0.0006179841,0.02541501,0.0009656959,0.000006194084],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2689188,0.002244984,0.7027977,0.003170102,0.0004386009,0.0001807821,0.001868269,0.01125382,0.009127038],"genre_scores_gemma":[0.8352265,0.0005218911,0.1528434,0.001320519,0.000134698,0.0001510449,0.004780727,0.0006241949,0.004396965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005991764,"threshold_uncertainty_score":0.01191378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257783878982921,"score_gpt":0.2643984327051478,"score_spread":0.2386200448068558,"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."}}