{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006011104,0.002025806,0.004778017,0.003503743,0.002025428,0.006297521,0.008323451,0.006503798,0.01830346],"category_scores_gemma":[0.03688941,0.001786205,0.002435733,0.003935351,0.005215693,0.01265383,0.003252903,0.006266941,0.002645884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005282579,"about_ca_system_score_gemma":0.001747629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01065423,"about_ca_topic_score_gemma":0.008505534,"domain_scores_codex":[0.9962525,0.001759798,0.0001590152,0.0005441246,0.0006689029,0.0006157663],"domain_scores_gemma":[0.9689435,0.02498971,0.001469487,0.001449683,0.001723567,0.001423994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006518835,0.00005932297,0.0004453617,0.0001349172,0.00005483193,0.00007442641,0.0002663335,0.09563711,0.0002720949,0.8913303,0.00615376,0.005506455],"study_design_scores_gemma":[0.00002661989,0.00001249251,0.0001214408,0.00002068017,0.00002173438,0.00003145913,0.00005133047,0.4689888,0.00004427513,0.5297236,0.000938083,0.00001946667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05955545,0.003655235,0.8894302,0.006138733,0.0004853556,0.0002424766,0.000996398,0.0008914949,0.03860477],"genre_scores_gemma":[0.880125,0.004991588,0.05115781,0.001106312,0.001660595,0.0009613969,0.0009806324,0.0006188222,0.05839793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01830346,"threshold_uncertainty_score":0.06123114,"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."}}