{"id":"W3093823951","doi":"10.1016/j.patcog.2023.109960","title":"Graph fairing convolutional networks for anomaly detection","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Computer science; Graph; Theoretical computer science; Anomaly detection; Convolution (computer science); Node (physics); Benchmark (surveying); Convolutional neural network; Algorithm; Pattern recognition (psychology); Artificial intelligence; Artificial neural network","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":[0.001287936,0.0008922227,0.0008962417,0.001645299,0.0005975799,0.0009784558,0.0019832,0.001424442,0.002900882],"category_scores_gemma":[0.004357292,0.0004011855,0.0007079486,0.001180036,0.001128293,0.00240843,0.001242182,0.001679088,0.0005076288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001632529,"about_ca_system_score_gemma":0.001239477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01430432,"about_ca_topic_score_gemma":0.01773103,"domain_scores_codex":[0.9993886,0.0001182949,0.00002652964,0.0002174261,0.000148607,0.0001006143],"domain_scores_gemma":[0.9979318,0.0008729665,0.0001631284,0.0005348267,0.0003900082,0.0001072625],"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":[0.0005133159,0.0002064161,0.00469015,0.0001519881,0.0001923953,0.0001591584,0.0000899415,0.5052522,0.01423376,0.08019193,0.009474097,0.3848447],"study_design_scores_gemma":[0.00000351455,0.00001679161,0.000221307,0.000003523175,0.000009472867,0.00001212221,0.000004008464,0.9713454,0.001562166,0.02639204,0.0004258026,0.00000387612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04083804,0.0005464418,0.9540606,0.0003986945,0.0001080059,0.00004157476,0.0002865897,0.002291372,0.001428714],"genre_scores_gemma":[0.8061073,0.0005335709,0.1820725,0.0002595553,0.0001339704,0.00006382399,0.001113669,0.0003140733,0.009401507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01430432,"threshold_uncertainty_score":0.02844208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03351837487831204,"score_gpt":0.2561009058031412,"score_spread":0.2225825309248291,"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."}}