{"id":"W2102920490","doi":"10.1145/2124295.2124368","title":"Maximizing product adoption in social networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":169,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Product (mathematics); Set (abstract data type); Computer science; Opinion leadership; Viral marketing; Social network (sociolinguistics); New product development; Key (lock); Work (physics); Maximization; Marketing; Business; Advertising; World Wide Web; Psychology; Social psychology; Social media; Public relations; Engineering; Mathematics; Computer security; Political science","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":[0.004017757,0.001635477,0.00193856,0.002007457,0.0008852594,0.002191331,0.001626353,0.002077334,0.003321121],"category_scores_gemma":[0.02241027,0.0009124842,0.0007724668,0.002844435,0.001178647,0.00613192,0.00204277,0.000894438,0.000776158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141092,"about_ca_system_score_gemma":0.0007945221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001246837,"about_ca_topic_score_gemma":0.001476476,"domain_scores_codex":[0.9967069,0.001723235,0.0001235901,0.0007015637,0.0004977124,0.0002470136],"domain_scores_gemma":[0.987336,0.009899382,0.0011295,0.0006136014,0.00067425,0.0003473118],"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":[0.0007701921,0.0007588891,0.02143279,0.001812468,0.0006182394,0.0006898225,0.001406627,0.5604888,0.01134812,0.1719766,0.005618633,0.2230789],"study_design_scores_gemma":[0.00008290808,0.0004720813,0.005900411,0.0001017829,0.0002076589,0.0004044328,0.0003934162,0.8162157,0.002532814,0.1672415,0.006394527,0.00005273745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3923675,0.003428487,0.5699922,0.001984743,0.00007606272,0.0006355203,0.0003840663,0.0003859297,0.03074543],"genre_scores_gemma":[0.945686,0.001367168,0.04853081,0.000108688,0.00008642067,0.0002872646,0.0001323495,0.0000673671,0.0037339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004017757,"threshold_uncertainty_score":0.02124816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02053242328576662,"score_gpt":0.2761302357370934,"score_spread":0.2555978124513268,"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."}}