{"id":"W192312889","doi":"","title":"k-Anonymization of Social Networks by Vertex Addition.","year":2011,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Vertex (graph theory); Node (physics); Computer science; Clustering coefficient; Cluster analysis; Modulo; Approximation algorithm; Constraint (computer-aided design); Social network (sociolinguistics); Theoretical computer science; Set (abstract data type); Combinatorics; Graph; Mathematics; Discrete mathematics; Algorithm; Artificial intelligence","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":[0.001811117,0.000926278,0.0009477924,0.001020378,0.001825378,0.001909773,0.001581569,0.001083127,0.002161414],"category_scores_gemma":[0.006736682,0.0004753966,0.001305252,0.00178806,0.001644496,0.004024165,0.002673089,0.001550888,0.0006049143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001386424,"about_ca_system_score_gemma":0.001205479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001398746,"about_ca_topic_score_gemma":0.002888494,"domain_scores_codex":[0.9970951,0.001381088,0.000120666,0.0006910408,0.0004868916,0.0002252657],"domain_scores_gemma":[0.9936078,0.002706649,0.0008061734,0.002379639,0.0002800182,0.0002198419],"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":[0.0007850229,0.000222994,0.002388149,0.0004508072,0.0002070814,0.0005812797,0.001007702,0.6028994,0.0186507,0.2286792,0.01046727,0.1336604],"study_design_scores_gemma":[0.00007977977,0.0001136148,0.0006628103,0.00005359978,0.00008628915,0.000577324,0.0003951941,0.6811469,0.01616568,0.2862809,0.01439918,0.00003860588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1201466,0.0004267764,0.8629951,0.001351422,0.000142297,0.0004541672,0.001348195,0.0007574317,0.01237797],"genre_scores_gemma":[0.6907057,0.0004480105,0.3006353,0.0002441668,0.00008985693,0.0002668318,0.00157158,0.0001362486,0.005902253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002161414,"threshold_uncertainty_score":0.01005924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02772801468186429,"score_gpt":0.2351919263085799,"score_spread":0.2074639116267156,"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."}}