{"id":"W4386609140","doi":"10.1109/tnnls.2023.3309632","title":"Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge Classification","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Discriminative model; Graph; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Encoder; Domain adaptation; Domain (mathematical analysis); Theoretical computer science; Pattern recognition (psychology); Machine learning; Mathematics","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","sts"],"consensus_categories":[],"category_scores_codex":[0.0009802828,0.0004784802,0.0005159201,0.0002239014,0.002319201,0.0005848571,0.0005816195,0.0003085173,0.000003119036],"category_scores_gemma":[0.000007094643,0.0004584531,0.0003366694,0.002269544,0.0001718829,0.0006823234,0.00001334679,0.001072274,0.00001867939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005541715,"about_ca_system_score_gemma":0.00002057939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001823814,"about_ca_topic_score_gemma":0.00001698063,"domain_scores_codex":[0.9961053,0.0005501639,0.0007015456,0.001090967,0.0003846762,0.001167398],"domain_scores_gemma":[0.9973794,0.001218816,0.0003330652,0.0005987809,0.0001987211,0.0002712477],"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":[0.0001087392,0.00002657947,0.0006132231,0.00003087914,0.00007113878,0.00001101848,0.00008977496,0.9752629,0.00004335261,0.004873069,0.001452232,0.01741711],"study_design_scores_gemma":[0.0008421501,0.000432638,0.004134556,0.000148523,0.00002985259,0.0000393844,0.0001043008,0.9897267,0.000002229326,0.0009192947,0.003135287,0.0004851301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02708466,0.0006177637,0.9642201,0.0002788051,0.005387156,0.001159976,0.000008128623,0.001180226,0.00006316662],"genre_scores_gemma":[0.9918828,0.0002922649,0.004682732,0.0001791911,0.001233804,0.0005768035,0.00002849544,0.00008096513,0.001042953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9647982,"threshold_uncertainty_score":0.9997867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0290642462466722,"score_gpt":0.2674654404000126,"score_spread":0.2384011941533404,"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."}}