{"id":"W4392098640","doi":"10.1007/s40747-024-01352-z","title":"Bi-DNE: bilayer evolutionary pattern preserved embedding for dynamic networks","year":2024,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Embedding; Computer science; Network formation; Node (physics); Process (computing); Network topology; Dynamic network analysis; Topology (electrical circuits); Evolutionary algorithm; Range (aeronautics); Artificial intelligence; Theoretical computer science; Distributed computing; Mathematics; Engineering; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004107174,0.0003903426,0.0005278204,0.0002542988,0.0002468804,0.0003911437,0.0005502355,0.00008672298,0.0007941879],"category_scores_gemma":[0.000006252453,0.0003616364,0.0005605975,0.0004680103,0.00006137002,0.0001677123,0.0002147299,0.0002673469,0.0001190426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001983039,"about_ca_system_score_gemma":0.00005312862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004626386,"about_ca_topic_score_gemma":0.00003181333,"domain_scores_codex":[0.997483,0.0001368466,0.0007985833,0.0006916822,0.0002776766,0.0006122377],"domain_scores_gemma":[0.9985045,0.0003683575,0.0001596104,0.0006496756,0.0001748616,0.0001429962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000860213,0.0005914141,0.03532973,0.001300304,0.003951466,0.00002556994,0.0006826778,0.2729399,0.002259292,0.07353359,0.4981147,0.1111853],"study_design_scores_gemma":[0.00007731331,0.00004678145,0.0003879822,0.0003523669,0.0001061825,0.000003624536,0.0001671449,0.8716354,0.0000568648,0.002467193,0.1243487,0.000350424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006567745,0.00432231,0.9842176,0.0001154431,0.001322991,0.001256527,0.0001261719,0.0005537065,0.001517465],"genre_scores_gemma":[0.993696,0.00002287235,0.00080902,0.00002620963,0.001617737,0.000650705,0.000696584,0.00009370501,0.002387103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9871283,"threshold_uncertainty_score":0.9998835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03712383603290929,"score_gpt":0.3280002523926515,"score_spread":0.2908764163597422,"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."}}