{"id":"W4385687128","doi":"10.1093/comnet/cnad028","title":"Hypergraph Artificial Benchmark for Community Detection (h–ABCD)","year":2023,"lang":"en","type":"article","venue":"Journal of Complex Networks","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hypergraph; Benchmark (surveying); Computer science; Community structure; Graph; Ground truth; Random graph; Algorithm; Theoretical computer science; Artificial intelligence; Discrete mathematics; Mathematics; Combinatorics; Cartography","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":[0.004279549,0.000663752,0.0007502793,0.001927735,0.001016514,0.001713532,0.00194688,0.002069378,0.002160416],"category_scores_gemma":[0.01723284,0.0003065247,0.0008387927,0.001479494,0.00149055,0.001716689,0.001728287,0.001717246,0.0004310189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001868854,"about_ca_system_score_gemma":0.0009482467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006495038,"about_ca_topic_score_gemma":0.005388908,"domain_scores_codex":[0.9965289,0.002063101,0.00009551952,0.0006537841,0.0005193223,0.0001393012],"domain_scores_gemma":[0.9834743,0.01067204,0.0008400945,0.002139711,0.002106785,0.0007670609],"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":[0.0006141788,0.0003350776,0.008604618,0.0004681833,0.0002652956,0.0002836231,0.0001887204,0.8102675,0.004602872,0.07553682,0.03123583,0.06759731],"study_design_scores_gemma":[0.000044365,0.00005718307,0.0006325147,0.00001312065,0.00001022315,0.00006960377,0.00002142019,0.9725425,0.001397826,0.02241737,0.002779476,0.00001447823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3310919,0.002773234,0.6318345,0.005637328,0.001167529,0.0004283059,0.005602672,0.00314582,0.01831868],"genre_scores_gemma":[0.8148412,0.0003742097,0.1749716,0.001067503,0.0002124848,0.0002550214,0.004698413,0.0003145148,0.003265008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006495038,"threshold_uncertainty_score":0.02263272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04547895218473307,"score_gpt":0.3071416032323553,"score_spread":0.2616626510476222,"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."}}