{"id":"W1990570693","doi":"10.1109/icdm.2011.132","title":"SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model","year":2011,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":505,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Maximization; Greedy algorithm; Computer science; Set (abstract data type); Time complexity; Running time; Algorithm; Approximation algorithm; Simple (philosophy); Mathematical optimization; 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":[0.001513159,0.001312426,0.001408153,0.001964648,0.0007567487,0.001224309,0.002304578,0.001150693,0.00437024],"category_scores_gemma":[0.005907147,0.0006460727,0.001227913,0.002550442,0.0009226913,0.002535688,0.001943363,0.001235952,0.001956303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001262378,"about_ca_system_score_gemma":0.002535506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003922119,"about_ca_topic_score_gemma":0.006192392,"domain_scores_codex":[0.9990707,0.0003023317,0.00005192946,0.0002143841,0.0002655244,0.0000951877],"domain_scores_gemma":[0.9977317,0.001444045,0.0001643106,0.0003288963,0.0002444763,0.00008665913],"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":[0.0003166561,0.0001860656,0.001994767,0.0003537247,0.000169162,0.0002118969,0.0002596395,0.5311828,0.006539203,0.03731705,0.01828723,0.4031819],"study_design_scores_gemma":[0.00003901593,0.00001924552,0.00007866153,0.000005968986,0.000009196722,0.0000417973,0.00001345807,0.9811458,0.001225428,0.01595323,0.001462264,0.000006007609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006047283,0.0001484217,0.9884689,0.0001345828,0.00002300742,0.0001213003,0.0001945858,0.003137592,0.00172435],"genre_scores_gemma":[0.1263322,0.0002181173,0.8692215,0.0001441762,0.00006344209,0.0004567286,0.0008475204,0.0005433151,0.002172981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00437024,"threshold_uncertainty_score":0.01461995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04184048520806127,"score_gpt":0.2850095289828672,"score_spread":0.243169043774806,"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."}}