{"id":"W2971325383","doi":"10.48550/arxiv.1906.07159","title":"vGraph: A Generative Model for Joint Community Detection and Node\\n Representation Learning","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; HEC Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Generative model; Node (physics); Representation (politics); Inference; Graph; Feature learning; Parameterized complexity; Generative grammar; Community structure; Theoretical computer science; Machine learning; Joint probability distribution; Artificial intelligence; Algorithm; 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":[0.0006822646,0.0005963561,0.0008505757,0.0004331082,0.001323682,0.0001981945,0.0004534231,0.0003175484,0.00003673222],"category_scores_gemma":[0.00002997662,0.0007783695,0.0007176163,0.000577613,0.000221897,0.0003678829,0.001195422,0.001855649,0.00001190526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002693982,"about_ca_system_score_gemma":0.0001323756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001572489,"about_ca_topic_score_gemma":0.0002932492,"domain_scores_codex":[0.9968223,0.0009350452,0.00048214,0.001197906,0.00009639846,0.0004662154],"domain_scores_gemma":[0.9969891,0.0003391384,0.0008892407,0.001114646,0.0004985556,0.0001693582],"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":[0.0001988137,0.0001851631,0.01195729,0.0001000145,0.0006069971,0.000001423145,0.001339121,0.9711569,0.00173535,0.009484375,0.00006955279,0.003165009],"study_design_scores_gemma":[0.0008706312,0.0001552267,0.0009157321,0.000117345,0.0007691559,5.823067e-7,0.001552983,0.9318393,0.001587025,0.06142656,0.0001220726,0.000643425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4310619,0.00002254212,0.5673628,0.00001363292,0.00008294878,0.0007933063,0.00003250768,0.00007649998,0.0005538415],"genre_scores_gemma":[0.9943366,0.0002246993,0.002479093,0.000022396,0.0001722344,0.00001752019,0.0002780542,0.00005937879,0.002410026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5648838,"threshold_uncertainty_score":0.9999765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1358008619107201,"score_gpt":0.2308524273954221,"score_spread":0.09505156548470195,"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."}}