{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000262925,0.0006047541,0.0005618731,0.0004298502,0.0009788235,0.0002556073,0.001497212,0.0002619541,0.00001618165],"category_scores_gemma":[0.00007622975,0.0005892991,0.0002189089,0.003501041,0.0004391371,0.0006923182,0.0003144718,0.0005233751,0.00002664237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001453671,"about_ca_system_score_gemma":0.0003106334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004507139,"about_ca_topic_score_gemma":0.0002658511,"domain_scores_codex":[0.9960549,0.0001499115,0.0008174509,0.001562081,0.0004250509,0.0009905868],"domain_scores_gemma":[0.9964287,0.000366421,0.0005486265,0.001714453,0.0006879363,0.0002538729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002175418,0.0003433334,0.802028,0.0002284733,0.0003335897,0.00002157905,0.0005936566,0.02157708,0.009875604,0.1435518,0.001098167,0.02013114],"study_design_scores_gemma":[0.002659152,0.0004831087,0.5079896,0.0004473615,0.0002523636,0.00001693511,0.00008939948,0.4599446,0.004639716,0.01781189,0.00447832,0.001187518],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0867674,0.0004403971,0.8998119,0.009317706,0.00154477,0.001461045,0.00003234905,0.0002606485,0.0003638502],"genre_scores_gemma":[0.9700298,0.0001461567,0.02537696,0.002744041,0.000187518,0.0002473234,0.00008980163,0.00005763199,0.001120706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8832625,"threshold_uncertainty_score":0.9996558,"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."}}