{"id":"W2041670710","doi":"10.1109/dasc.2011.141","title":"Finding Strong Groups of Friends among Friends in Social Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Friendship; Popularity; Casual; Social media; Internet privacy; Social network (sociolinguistics); World Wide Web; Interdependence; Computer science; Face (sociological concept); Social group; Psychology; Social psychology; Sociology; Political science; Social science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001718073,0.0007696647,0.0009415775,0.007506012,0.001809368,0.001631054,0.001000044,0.001316679,0.001657774],"category_scores_gemma":[0.01298816,0.000575982,0.0008401169,0.005214288,0.001040103,0.004297388,0.001803145,0.000697792,0.0007615536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005291374,"about_ca_system_score_gemma":0.0004173737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002320178,"about_ca_topic_score_gemma":0.003331754,"domain_scores_codex":[0.9971046,0.001028439,0.0002056479,0.0007512709,0.0007252272,0.0001848757],"domain_scores_gemma":[0.9913787,0.00500688,0.001657453,0.0008957686,0.0006971491,0.0003640253],"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":[0.0009038181,0.0008786151,0.4426208,0.001531282,0.001589401,0.003464968,0.01177509,0.04789914,0.01303903,0.04339734,0.01376294,0.4191376],"study_design_scores_gemma":[0.0001379973,0.0005933103,0.1774196,0.0004147934,0.0009708704,0.005800587,0.01427192,0.583556,0.01334368,0.1534901,0.04977114,0.0002300067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7368109,0.002838598,0.2451981,0.001285659,0.0001216381,0.0006421511,0.002385595,0.0005577687,0.01015959],"genre_scores_gemma":[0.9349209,0.0008103632,0.06080757,0.00009504989,0.000118627,0.0002195255,0.001432533,0.00002885508,0.001566713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007506012,"threshold_uncertainty_score":0.009086132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03029972686551392,"score_gpt":0.2673251478918135,"score_spread":0.2370254210262996,"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."}}