{"id":"W4414427902","doi":"10.1145/3749156","title":"A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Management of Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Nanyang Technological University; Ministry of Education - Singapore","keywords":"Benchmarking; Graph; Computation; Benchmark (surveying); Power graph analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002263535,0.001547973,0.0009944419,0.0013298,0.0007921295,0.001511725,0.001873747,0.001695682,0.003391187],"category_scores_gemma":[0.01357295,0.000331977,0.0008192121,0.001260603,0.001075999,0.003761234,0.001339257,0.001510874,0.001077448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001849986,"about_ca_system_score_gemma":0.00183975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009189421,"about_ca_topic_score_gemma":0.01157457,"domain_scores_codex":[0.9986529,0.0003347236,0.0001000502,0.000329168,0.0003675241,0.0002156267],"domain_scores_gemma":[0.9950089,0.002661868,0.0002033629,0.001040265,0.0008963341,0.000189281],"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":[0.0008076993,0.0004219628,0.004741617,0.001107908,0.0002222813,0.0001753167,0.0001589423,0.729959,0.00834077,0.02648868,0.01426778,0.213308],"study_design_scores_gemma":[0.00006093071,0.0002560268,0.0007742235,0.00008439938,0.00005349481,0.0001410167,0.000142197,0.9645317,0.01076563,0.01872789,0.004434336,0.00002818169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4522813,0.007020213,0.4893217,0.002442744,0.0009083007,0.00044166,0.00280787,0.01393755,0.03083869],"genre_scores_gemma":[0.7965266,0.001797562,0.1930272,0.0006968562,0.00008587243,0.0002322962,0.00374859,0.0008970127,0.002988137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009189421,"threshold_uncertainty_score":0.01827186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864368781006588,"score_gpt":0.3161488555937306,"score_spread":0.2875051677836648,"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."}}