{"id":"W2159176819","doi":"10.1609/aaai.v26i1.8204","title":"Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Maximization; Heuristics; Computer science; Mathematical optimization; Greedy algorithm; Heuristic; Constraint (computer-aided design); Set (abstract data type); Process (computing); Algorithm; Mathematics; 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":[0.002025636,0.001132279,0.001237921,0.001255605,0.0008715027,0.001569485,0.001587324,0.001277176,0.001661057],"category_scores_gemma":[0.007894456,0.0007356948,0.000919141,0.001591762,0.001590191,0.002471997,0.001289344,0.001186164,0.0002277317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002245594,"about_ca_system_score_gemma":0.001360668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005033753,"about_ca_topic_score_gemma":0.004327615,"domain_scores_codex":[0.9988518,0.0003929347,0.00003902646,0.0002790086,0.000254477,0.0001827354],"domain_scores_gemma":[0.9950442,0.003782352,0.0004976459,0.0001501058,0.0002709297,0.0002548092],"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":[0.00009513034,0.00005108132,0.0007943704,0.0001067195,0.00005002314,0.0001857263,0.0001596027,0.9180436,0.002628519,0.06348558,0.0007938967,0.01360574],"study_design_scores_gemma":[0.000016673,0.00002333644,0.0001604318,0.000005507924,0.00001474168,0.00003311939,0.00002835081,0.9663377,0.0009369227,0.03183616,0.0005999511,0.000007104966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06990179,0.0004039615,0.923525,0.0004398546,0.0000244619,0.0001012616,0.0001032628,0.0001789812,0.005321383],"genre_scores_gemma":[0.8782809,0.0007789738,0.1168173,0.0001144887,0.00008904219,0.0002063807,0.000147371,0.00008288286,0.003482794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005033753,"threshold_uncertainty_score":0.01629299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02270244581810292,"score_gpt":0.2931075849506718,"score_spread":0.2704051391325689,"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."}}