{"id":"W2997242078","doi":"10.1609/aaai.v34i04.6178","title":"DGE: Deep Generative Network Embedding Based on Commonality and Individuality","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Zhejiang University; China Postdoctoral Science Foundation; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Embedding; Computer science; Node (physics); Generative model; Network topology; Topology (electrical circuits); Artificial intelligence; Generative grammar; Focus (optics); Theoretical computer science; Variety (cybernetics); Machine learning; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005202856,0.0003019088,0.0003541025,0.0000518756,0.0003583855,0.000301355,0.00166098,0.0001029626,0.00002236111],"category_scores_gemma":[0.0004268435,0.0002328518,0.00011064,0.0009422241,0.0003720005,0.0004264513,0.0005573189,0.0005621373,0.00001509239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003010661,"about_ca_system_score_gemma":0.00005126359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007902811,"about_ca_topic_score_gemma":0.0000068961,"domain_scores_codex":[0.997659,0.00006718196,0.0005332736,0.0007326664,0.0005766079,0.0004312673],"domain_scores_gemma":[0.9984018,0.0003057045,0.000433566,0.0003003288,0.000354886,0.0002037751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001377824,0.0000888348,0.001731574,0.00003889055,0.00001733725,0.000001272958,0.001266315,0.01994327,0.002371599,0.9234718,0.0002603749,0.05067092],"study_design_scores_gemma":[0.00003994367,0.0002886583,0.0007550798,0.0001148583,0.000009639659,0.000001215932,0.0001013441,0.792501,0.05375442,0.1521054,0.00007810074,0.000250335],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1826576,0.00009299415,0.7630687,0.04481866,0.0007344077,0.001328254,0.00001951675,0.0003941573,0.006885622],"genre_scores_gemma":[0.9780357,0.00001549341,0.01739978,0.00434823,0.0001557309,0.00002119708,8.126589e-7,0.00001295714,0.00001012508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.795378,"threshold_uncertainty_score":0.9495421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09933017429236297,"score_gpt":0.3111534523431887,"score_spread":0.2118232780508257,"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."}}