{"id":"W2406750717","doi":"","title":"Frequent subgraph mining from streams of linked graph structured data","year":2015,"lang":"en","type":"article","venue":"Mspace (University of Manitoba)","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data stream mining; Disjoint sets; Graph; Big data; Theoretical computer science; Data mining; Knowledge graph; Information retrieval; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001739741,0.000101719,0.0001942447,0.0001254004,0.0000884031,0.00002796972,0.002396829,0.00006272776,0.00000280414],"category_scores_gemma":[0.00002178165,0.0001215137,0.00004495253,0.0004310627,0.0001270405,0.0006022459,0.000965473,0.00008465666,0.000008061056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002292604,"about_ca_system_score_gemma":0.00008221856,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008156258,"about_ca_topic_score_gemma":0.04028738,"domain_scores_codex":[0.9990264,0.00003965577,0.0001015579,0.0003915784,0.0002919906,0.0001487997],"domain_scores_gemma":[0.9980471,0.00005609182,0.0002012186,0.00144414,0.0001264786,0.0001249573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001557735,0.001128542,0.3079985,0.000138644,0.001095453,0.0001948329,0.01469715,0.0007363891,0.01326862,0.04922327,0.1584487,0.4529142],"study_design_scores_gemma":[0.00471495,0.0004913323,0.6273649,0.0002409588,0.0002995459,0.00001466171,0.05612447,0.2614464,0.002283361,0.01425366,0.03158444,0.001181307],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7719203,0.0001256275,0.2255396,0.0009424981,0.0001758941,0.000116677,0.0006262379,0.00008490873,0.0004682069],"genre_scores_gemma":[0.6327682,0.00002618206,0.3668458,0.00001729225,0.00003351715,1.489284e-7,0.0002772879,0.000006065443,0.00002555781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4517329,"threshold_uncertainty_score":0.9984485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05193318104658357,"score_gpt":0.2312774648480558,"score_spread":0.1793442838014722,"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."}}