{"id":"W2949559065","doi":"10.48550/arxiv.1906.07159","title":"vGraph: A Generative Model for Joint Community Detection and Node Representation Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Generative model; Node (physics); Representation (politics); Inference; Graph; Feature learning; Parameterized complexity; Generative grammar; Community structure; Joint probability distribution; Theoretical computer science; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002215508,0.001253588,0.001394962,0.002930661,0.0009189209,0.001622212,0.004820158,0.00246613,0.003258069],"category_scores_gemma":[0.007514435,0.001195282,0.001975517,0.002654279,0.001833222,0.003143074,0.002480032,0.002680241,0.001492559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179184,"about_ca_system_score_gemma":0.001415346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009003672,"about_ca_topic_score_gemma":0.01728987,"domain_scores_codex":[0.9986812,0.0005027957,0.00003363942,0.0004586836,0.000218874,0.0001047855],"domain_scores_gemma":[0.9974746,0.001480806,0.0002168331,0.0004462439,0.0002302277,0.0001512421],"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":[0.0001525754,0.000140249,0.004658544,0.0001913699,0.0002627599,0.0003763257,0.0004466827,0.6744447,0.003590492,0.1528411,0.02008907,0.1428061],"study_design_scores_gemma":[0.00001159919,0.000008720051,0.0001658406,0.00001351938,0.00001116672,0.00006690057,0.00001642375,0.9386812,0.0004135383,0.0585634,0.002038749,0.000008992481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005485113,0.0001953483,0.9919721,0.0002300016,0.00003399805,0.00005242772,0.0003646175,0.0009292365,0.0007372542],"genre_scores_gemma":[0.3636267,0.0007119146,0.6200976,0.0008405327,0.000195439,0.0005601989,0.004759761,0.001129986,0.008077814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009003672,"threshold_uncertainty_score":0.01790255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1288917166239386,"score_gpt":0.2329724560619568,"score_spread":0.1040807394380182,"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."}}