{"id":"W2983727063","doi":"10.1371/journal.pone.0224307","title":"Clustering via hypergraph modularity","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Narodowa Agencja Wymiany Akademickiej","keywords":"Hypergraph; Modularity (biology); Cluster analysis; Computer science; Theoretical computer science; Clustering coefficient; Heuristic; Function (biology); Simple (philosophy); Graph; Graph theory; Algorithm; Mathematics; Combinatorics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006570075,0.00008897368,0.0001959389,0.00004739952,0.0000392869,0.00002481518,0.0001405325,0.0000193476,0.001215617],"category_scores_gemma":[7.703719e-7,0.00009228812,0.00008561949,0.0001390611,0.0000114547,0.00006668297,0.0000979574,0.0001117671,0.0001959314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000107628,"about_ca_system_score_gemma":0.000005241969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001331359,"about_ca_topic_score_gemma":0.000006392922,"domain_scores_codex":[0.999372,0.00001948783,0.0001215262,0.0001766718,0.0001485443,0.0001617354],"domain_scores_gemma":[0.9995275,0.00001669037,0.000044414,0.0003347221,0.00003703711,0.00003960993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001473888,0.001752553,0.8523638,0.00005209969,0.001029854,8.545739e-7,0.000089229,0.000193582,0.1255107,0.005196111,0.0002999609,0.01349647],"study_design_scores_gemma":[0.00138298,0.0002638983,0.05814889,0.0004296392,0.0008534034,6.940559e-7,0.00009447028,0.6865801,0.1682451,0.08054298,0.001871544,0.00158625],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9541506,0.00002213871,0.02649028,0.00006166389,0.00001443695,0.0001635952,0.000003188042,0.0001090057,0.01898514],"genre_scores_gemma":[0.9903877,0.000001360743,0.008670454,0.0000310359,0.0001612267,0.00001622872,0.0000173657,0.00001384603,0.0007007828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.794215,"threshold_uncertainty_score":0.9996974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02184420045059696,"score_gpt":0.2148025363617362,"score_spread":0.1929583359111392,"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."}}