{"id":"W3130921348","doi":"10.1109/asonam49781.2020.9381460","title":"MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Maximization; Heuristic; Monte Carlo method; Machine learning; Set (abstract data type); Artificial intelligence; Social network (sociolinguistics); Competitor analysis; Mathematical optimization; Mathematics; Statistics; Social media","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":[0.003628094,0.001380223,0.001572065,0.002685413,0.000985028,0.001325249,0.003907404,0.001599928,0.002835624],"category_scores_gemma":[0.02084493,0.001043879,0.001275564,0.00154349,0.001948085,0.002476336,0.002528071,0.001899515,0.0009836142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001858168,"about_ca_system_score_gemma":0.001963325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004768313,"about_ca_topic_score_gemma":0.009050952,"domain_scores_codex":[0.9979391,0.0009663799,0.00007681373,0.0003808978,0.0004821942,0.0001546481],"domain_scores_gemma":[0.9875785,0.00968572,0.0007958727,0.0008834971,0.000791973,0.0002644671],"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":[0.0001072076,0.00009268984,0.003941094,0.000200786,0.0001243976,0.0001413502,0.0003011017,0.8644112,0.002861384,0.03162497,0.002925106,0.09326876],"study_design_scores_gemma":[0.000005811244,0.00001031627,0.00007500414,0.000008214055,0.000004439026,0.0000193186,0.000008920134,0.9915063,0.0006144442,0.007293637,0.0004487597,0.000004859719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009202677,0.0001078558,0.9881713,0.0001377709,0.00002026928,0.00008976865,0.0001376029,0.00102057,0.001112152],"genre_scores_gemma":[0.3410165,0.0002471386,0.6529094,0.0004229531,0.000140522,0.0007819023,0.001037268,0.000567568,0.002876723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004768313,"threshold_uncertainty_score":0.01918739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02469218895560351,"score_gpt":0.2742732335696557,"score_spread":0.2495810446140522,"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."}}