{"id":"W2108424850","doi":"10.1186/s40649-015-0013-8","title":"A study on the influential neighbors to maximize information diffusion in online social networks","year":2015,"lang":"en","type":"article","venue":"Computational Social Networks","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council; Iran Telecommunication Research Center; National Research Foundation of Korea; National IT Industry Promotion Agency; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Maximization; Viral marketing; Computer science; Diffusion; Social network (sociolinguistics); Control (management); Mathematical optimization; Data science; Operations research; Social media; Mathematics; World Wide Web; 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":[0.004255567,0.0009322646,0.001169907,0.001145009,0.0009264338,0.001567854,0.001771458,0.001358106,0.001639966],"category_scores_gemma":[0.02703027,0.0006152288,0.0007968229,0.001128211,0.001590852,0.00397257,0.001209345,0.001191643,0.0002342104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189534,"about_ca_system_score_gemma":0.0007131324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001728614,"about_ca_topic_score_gemma":0.00140363,"domain_scores_codex":[0.9981477,0.001150219,0.00005053756,0.0002997973,0.0002220188,0.000129709],"domain_scores_gemma":[0.9768811,0.01995841,0.0009222275,0.0008095635,0.000987979,0.0004407038],"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":[0.0002830668,0.0003355704,0.006384789,0.0004767659,0.0002134398,0.0004257388,0.001162143,0.6484701,0.01128858,0.2731701,0.003204452,0.05458513],"study_design_scores_gemma":[0.00003375594,0.0001050081,0.0005709297,0.00002127938,0.00004126173,0.0001079323,0.0000852848,0.9566708,0.001724391,0.03923379,0.001390058,0.00001559275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2156055,0.002028852,0.7633178,0.001606384,0.00006881245,0.0001797942,0.00008986191,0.000185882,0.01691714],"genre_scores_gemma":[0.9332159,0.0009812394,0.06270872,0.000153602,0.0001405246,0.0001249958,0.00005872161,0.00008435961,0.002531864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004255567,"threshold_uncertainty_score":0.02250582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03092943649076229,"score_gpt":0.3021121997038541,"score_spread":0.2711827632130918,"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."}}