{"id":"W2053553533","doi":"10.1145/1286240.1286291","title":"Identifying subcommunities using cohesive subgroups in social hypertext","year":2007,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hyperlink; Hypertext; World Wide Web; Computer science; Sense of community; The Internet; Group (periodic table); Connection (principal bundle); Social network (sociolinguistics); Web page; Psychology; Social media; Mathematics; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00264179,0.0005749717,0.0007669851,0.009137813,0.00234843,0.00324101,0.0007812883,0.0008997216,0.002217838],"category_scores_gemma":[0.01572355,0.0003762309,0.0008689786,0.005832773,0.00183003,0.005895343,0.003010944,0.0007017837,0.0004530542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000914,"about_ca_system_score_gemma":0.0008983279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00430659,"about_ca_topic_score_gemma":0.006415765,"domain_scores_codex":[0.996497,0.001467721,0.0003471405,0.0008171877,0.0006996246,0.0001713505],"domain_scores_gemma":[0.9818361,0.01089024,0.002990945,0.002174524,0.001488624,0.0006197123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0012029,0.0007941336,0.2760811,0.00144238,0.0007441872,0.001175956,0.09650198,0.02379619,0.04292551,0.140613,0.00337195,0.4113506],"study_design_scores_gemma":[0.0001304526,0.000951503,0.1993511,0.0004523526,0.0007563271,0.001512255,0.07465555,0.3273408,0.0213509,0.3348098,0.03838735,0.0003016008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6460263,0.0003644887,0.3437912,0.0003363558,0.00002615379,0.001015054,0.0006906639,0.0004685543,0.00728126],"genre_scores_gemma":[0.8846731,0.0001177712,0.1127131,0.00003139866,0.00002925415,0.0005905572,0.000660021,0.00003665096,0.001148204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009137813,"threshold_uncertainty_score":0.01397133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06131479344972673,"score_gpt":0.3369482575123982,"score_spread":0.2756334640626714,"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."}}