{"id":"W2150860159","doi":"10.1016/j.dam.2012.03.030","title":"Improving heuristics for network modularity maximization using an exact algorithm","year":2012,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Heuristics; Merge (version control); Maximization; Algorithm; Heuristic; Merge algorithm; Partition (number theory); Mathematics; Modularity (biology); Computer science; Mathematical optimization; Parallel computing","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":[],"consensus_categories":[],"category_scores_codex":[0.0004410286,0.0002442039,0.0003543069,0.00004506943,0.0002607135,0.00009573921,0.0002025923,0.00006363178,0.00006289042],"category_scores_gemma":[0.000006447345,0.0002399005,0.0001296876,0.0001926392,0.00003602674,0.0001861373,0.000105791,0.0001242909,0.000003767502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004129496,"about_ca_system_score_gemma":0.00002031702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003063257,"about_ca_topic_score_gemma":0.000001072571,"domain_scores_codex":[0.9986492,0.00001787672,0.0004032562,0.0002290864,0.0001770155,0.0005235947],"domain_scores_gemma":[0.9989149,0.00008852112,0.0003148927,0.0004764663,0.00006922609,0.0001359895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001975265,0.001086951,0.002224437,0.0002655596,0.0003907272,3.291221e-7,0.001385405,0.02093739,0.004088557,0.8232562,0.0007647577,0.1455799],"study_design_scores_gemma":[0.0001870629,0.00001487474,0.00003724742,0.00001612334,0.0002393186,5.84579e-7,0.0002411242,0.8841749,0.001156455,0.1133335,0.0002846239,0.0003141197],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01123951,0.00002398709,0.9865682,0.000003700866,0.00006554835,0.0005886673,0.0000414007,0.0001265841,0.001342358],"genre_scores_gemma":[0.4278713,5.812197e-7,0.5709118,0.00001276468,0.0008866676,0.00006243675,0.0001966297,0.00004447251,0.00001330481],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8632376,"threshold_uncertainty_score":0.978286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02769404798303304,"score_gpt":0.2873221680629435,"score_spread":0.2596281200799104,"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."}}