{"id":"W3096565974","doi":"10.1007/s10115-020-01521-9","title":"CANE: community-aware network embedding via adversarial training","year":2020,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Novelis (Canada)","funders":"","keywords":"Computer science; Node (physics); Discriminative model; Pairwise comparison; Embedding; Adversarial system; Machine learning; Representation (politics); Feature learning; Data mining; Artificial intelligence; Community structure; Theoretical computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001344089,0.001875419,0.001387966,0.001333416,0.0008016598,0.0009579289,0.003555262,0.003343785,0.009324199],"category_scores_gemma":[0.006385839,0.000754594,0.001041911,0.001303993,0.001070118,0.00280823,0.003476032,0.004111093,0.003838959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008890697,"about_ca_system_score_gemma":0.001094218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00683907,"about_ca_topic_score_gemma":0.01355735,"domain_scores_codex":[0.9992988,0.0002476155,0.00001767821,0.0001844413,0.0001695189,0.00008192426],"domain_scores_gemma":[0.9978848,0.001017063,0.0001026612,0.000571942,0.0002824718,0.0001409462],"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":[0.0003759533,0.0003687874,0.0008961227,0.0002804911,0.0002292055,0.0002497818,0.0001178687,0.6152396,0.004554525,0.03283675,0.05879717,0.2860537],"study_design_scores_gemma":[0.0000155944,0.00001869482,0.00004987378,0.00001030606,0.000007528077,0.00002817543,0.000008088256,0.9857538,0.0006550082,0.01185607,0.001590587,0.000006298671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008892289,0.0004938703,0.9784291,0.0006705748,0.000296603,0.0001772742,0.0008907954,0.006499246,0.003650283],"genre_scores_gemma":[0.3871111,0.0006046887,0.576538,0.001300102,0.0003367198,0.0007096621,0.006148761,0.002013223,0.02523783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009324199,"threshold_uncertainty_score":0.03119254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0314213780843983,"score_gpt":0.2559193188299602,"score_spread":0.2244979407455619,"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."}}