{"id":"W1502487903","doi":"10.1007/978-3-642-17572-5_48","title":"Threshold Models for Competitive Influence in Social Networks","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":316,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Maximization; Competitor analysis; Greedy algorithm; Mathematical optimization; Cascade; Set (abstract data type); Square root; Extension (predicate logic); Conjecture; Product (mathematics); Mathematical economics; Algorithm; Mathematics; Discrete mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004940854,0.0004281618,0.0006107463,0.0004364064,0.0002651991,0.0002110664,0.001131701,0.0002636797,0.00004714433],"category_scores_gemma":[0.000005596863,0.0004278721,0.0002136136,0.0003580242,0.0006700816,0.0002664824,0.0004879179,0.001201511,0.000002465876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001171461,"about_ca_system_score_gemma":0.0001921916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006524856,"about_ca_topic_score_gemma":0.0003284543,"domain_scores_codex":[0.997664,0.00001427701,0.0004616897,0.0009225124,0.0003576188,0.000579887],"domain_scores_gemma":[0.9986364,0.0003142839,0.0002535571,0.00047726,0.0002460481,0.00007243615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001496689,0.00004084781,0.0007766795,0.00001120399,0.00002383516,0.000004315611,0.0003351518,0.4907555,0.0000614468,0.374411,0.00002352745,0.1335415],"study_design_scores_gemma":[0.000133345,0.00002444437,0.00009134825,0.0001051138,0.00001005513,4.725557e-7,1.857337e-7,0.4752453,0.0001004227,0.5235688,0.0004162231,0.0003043168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004885605,0.0000622828,0.9910991,0.0001243883,0.0002143317,0.0005563539,0.00001789195,0.00005477476,0.007382324],"genre_scores_gemma":[0.9420043,0.000004208502,0.05596889,0.000323124,0.001494687,0.00005161789,0.00003483873,0.00004023205,0.00007808892],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9415157,"threshold_uncertainty_score":0.9998173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530642877649156,"score_gpt":0.2650131315491454,"score_spread":0.2497067027726538,"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."}}