{"id":"W2212555590","doi":"","title":"Identifying important nodes in heterogenous networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scalability; Field (mathematics); Artificial intelligence; Machine learning; Set (abstract data type); Statistical model; Position paper; Statistical learning; Data modeling; Statistical relational learning; Data set; Data mining; Theoretical computer science; Relational database; Data science; World Wide Web; Database","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":[0.002451953,0.0008064132,0.0008120014,0.005308891,0.001255696,0.001823319,0.001381179,0.0013214,0.001606258],"category_scores_gemma":[0.0151038,0.000526852,0.0008355006,0.003081336,0.001617684,0.004414694,0.002388115,0.00143847,0.0003991007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001178217,"about_ca_system_score_gemma":0.0005348644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003239902,"about_ca_topic_score_gemma":0.00646492,"domain_scores_codex":[0.9984016,0.0005197083,0.00006789611,0.0005839253,0.0002945922,0.0001323283],"domain_scores_gemma":[0.9858958,0.00917882,0.002248978,0.001328172,0.0007880511,0.0005601537],"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":[0.0004999881,0.0002434814,0.1570777,0.0006418212,0.0006758404,0.001449142,0.003172286,0.3703571,0.01216749,0.2502297,0.009105871,0.1943797],"study_design_scores_gemma":[0.0000211248,0.00003656212,0.01610204,0.0000579187,0.0001268409,0.0003360978,0.0005732098,0.6752025,0.002300035,0.2989376,0.006276636,0.00002950844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2886783,0.001625252,0.7027116,0.0008687757,0.00006138073,0.0001113043,0.000942256,0.0003148509,0.004686269],"genre_scores_gemma":[0.9089932,0.0007478849,0.08677869,0.0001219451,0.0001348899,0.00009504305,0.00121647,0.00009502564,0.001816763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005308891,"threshold_uncertainty_score":0.01296735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181210372096583,"score_gpt":0.2525452804955544,"score_spread":0.2407331767745885,"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."}}