{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000244608,0.0002249425,0.000334855,0.0001905319,0.000388701,0.0000678081,0.0001982112,0.0001117412,0.00001542425],"category_scores_gemma":[0.000009289761,0.0002758299,0.00026068,0.000189856,0.0000634422,0.000123522,0.0005538727,0.0007845886,0.000002593152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007931834,"about_ca_system_score_gemma":0.00004177441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001168999,"about_ca_topic_score_gemma":0.0001411684,"domain_scores_codex":[0.9988639,0.0002726504,0.0001690434,0.00047422,0.00004130931,0.0001789276],"domain_scores_gemma":[0.9988616,0.0001106754,0.0002987046,0.0005044951,0.0001678284,0.00005668802],"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":[0.0000411707,0.00005266662,0.01233175,0.00003301154,0.0002201501,4.775376e-7,0.000458028,0.9778467,0.0006584782,0.00697456,0.00009037823,0.00129267],"study_design_scores_gemma":[0.000287474,0.00003190207,0.000578824,0.0000337376,0.0002041262,1.240519e-7,0.000414026,0.9133414,0.0009801588,0.08385396,0.00003707879,0.0002371896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4300157,0.000007451436,0.5688665,0.000006919046,0.00003329474,0.0002927872,0.0000155707,0.00006009435,0.0007016438],"genre_scores_gemma":[0.9966551,0.00002399071,0.001994384,0.00001188138,0.00008415896,0.000008662179,0.0001609993,0.00002330056,0.001037488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5668721,"threshold_uncertainty_score":0.9999694,"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."}}