{"id":"W2543722153","doi":"10.1109/tkde.2017.2740284","title":"Activity Maximization by Effective Information Diffusion in Social Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Submodular set function; Scalability; Maximization; Polling; Approximation algorithm; Heuristic; Upper and lower bounds; Social network (sociolinguistics); Mathematical optimization; Algorithm; Artificial intelligence; Social media; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006335279,0.001711823,0.002444399,0.002154956,0.0008388403,0.002362034,0.003056147,0.002112632,0.001828526],"category_scores_gemma":[0.02648151,0.00136988,0.001408265,0.002332972,0.003190647,0.005333093,0.002497831,0.002042774,0.000403564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002750047,"about_ca_system_score_gemma":0.001126022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003163791,"about_ca_topic_score_gemma":0.002950142,"domain_scores_codex":[0.9968727,0.001731395,0.000121041,0.0006649072,0.000396071,0.0002138539],"domain_scores_gemma":[0.9812148,0.01588449,0.00119559,0.000877679,0.0004948883,0.000332683],"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":[0.0001377481,0.00007660185,0.001586153,0.00024243,0.0001026938,0.0001022477,0.0002472738,0.8475561,0.001656148,0.1163433,0.001312319,0.03063694],"study_design_scores_gemma":[0.00001305791,0.0000161395,0.00013977,0.00001093667,0.00000855644,0.00001846903,0.00001393106,0.9514596,0.0003509022,0.04765987,0.0003001423,0.000008548936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02267579,0.0004137595,0.9746808,0.0003930181,0.00001933807,0.00007766149,0.00008599837,0.0001800024,0.001473692],"genre_scores_gemma":[0.7317803,0.001091662,0.2621874,0.0002782984,0.0001622106,0.0004902862,0.000380798,0.0001948529,0.003434247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006335279,"threshold_uncertainty_score":0.03350455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01055453571133445,"score_gpt":0.2678646365113213,"score_spread":0.2573101007999868,"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."}}