{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263056,0.001294713,0.001196111,0.001384667,0.000410817,0.001217806,0.002182537,0.001381037,0.002289788],"category_scores_gemma":[0.004347915,0.0007992339,0.001288999,0.001411288,0.001395001,0.002951986,0.002190637,0.003140877,0.0007256604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001238886,"about_ca_system_score_gemma":0.0008875669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00553301,"about_ca_topic_score_gemma":0.009571502,"domain_scores_codex":[0.999393,0.0002224759,0.00002208474,0.0001810293,0.0001214816,0.00005992678],"domain_scores_gemma":[0.9985014,0.000854344,0.0001665907,0.0002276764,0.0001459522,0.0001040022],"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":[0.00007233083,0.00008064418,0.00281469,0.0001300999,0.0001593738,0.0001556151,0.0002389802,0.7863241,0.002301036,0.08637439,0.005173876,0.1161748],"study_design_scores_gemma":[0.000004150672,0.00001103746,0.0001539943,0.00001106146,0.000008071258,0.00003027693,0.000009835558,0.966459,0.0002569853,0.03225365,0.0007947637,0.000007149511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008760175,0.0003291899,0.9890478,0.000242211,0.00002696808,0.00003169034,0.0002210602,0.0004633752,0.0008775707],"genre_scores_gemma":[0.627777,0.001377758,0.3573331,0.0006047997,0.0001434127,0.0003672102,0.002808666,0.0006196172,0.008968377],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00553301,"threshold_uncertainty_score":0.01100159,"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."}}