{"id":"W4376607990","doi":"10.1109/tnet.2023.3268982","title":"Graph-Tensor Neural Networks for Network Traffic Data Imputation","year":2023,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Computer science; Imputation (statistics); Artificial neural network; Data mining; Graph; Traffic generation model; Algorithm; Artificial intelligence; Missing data; Theoretical computer science; Machine learning; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002786332,0.001630129,0.001335743,0.001400641,0.0007246041,0.00109131,0.002229543,0.001268822,0.001730095],"category_scores_gemma":[0.01388562,0.0007125448,0.001184941,0.00250905,0.001198779,0.002962012,0.001532017,0.003796846,0.0007785537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001734954,"about_ca_system_score_gemma":0.002370198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01668111,"about_ca_topic_score_gemma":0.01433196,"domain_scores_codex":[0.9986738,0.0004628516,0.00008898072,0.0003474345,0.0002828303,0.0001441262],"domain_scores_gemma":[0.9958283,0.001725915,0.0004910745,0.0008970919,0.0009081523,0.0001493482],"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":[0.0001837609,0.00008190595,0.002664954,0.0001442185,0.0001085902,0.00009747466,0.00009363039,0.8229573,0.001815618,0.02245853,0.005681156,0.1437129],"study_design_scores_gemma":[0.000002249091,0.000006800515,0.0001115902,0.000004083811,0.00000399588,0.000007353165,0.000005200241,0.9907331,0.0003454034,0.008482517,0.0002926802,0.000004991245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00875048,0.0003208832,0.9883057,0.0003691243,0.00006435634,0.00002967663,0.0002948082,0.001371745,0.0004933033],"genre_scores_gemma":[0.5311186,0.001039241,0.4597346,0.0003864638,0.000211262,0.0002577458,0.003339936,0.0003734097,0.003538852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01668111,"threshold_uncertainty_score":0.03316802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05153299197999835,"score_gpt":0.2815334257800944,"score_spread":0.2300004338000961,"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."}}