{"id":"W4285290135","doi":"10.1007/978-3-031-08757-8_43","title":"A Sparse Matrix Approach for Covering Large Complex Networks by Cliques","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Heuristics; Clique; Pairwise comparison; Computer science; Benchmark (surveying); Theoretical computer science; Graph; Algorithm; Scalability; Computation; Heuristic; Mathematics; Mathematical optimization; Artificial intelligence; Combinatorics","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":[0.0007761757,0.0009985346,0.001276347,0.001339251,0.0007770064,0.001166735,0.002278738,0.001331836,0.005179895],"category_scores_gemma":[0.004475866,0.0008355117,0.001389498,0.002573752,0.001185789,0.002663599,0.001990191,0.002559253,0.0009324676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008697739,"about_ca_system_score_gemma":0.0005059391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004015879,"about_ca_topic_score_gemma":0.006304699,"domain_scores_codex":[0.9993609,0.0002422307,0.0000198987,0.0001496802,0.0001739439,0.00005343145],"domain_scores_gemma":[0.9971929,0.001971469,0.0001359907,0.0003587438,0.0002212466,0.0001196745],"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":[0.00007879252,0.00009407943,0.0004772734,0.0002931182,0.000105401,0.0001369533,0.0002683038,0.4808437,0.00572933,0.4004531,0.01395419,0.09756581],"study_design_scores_gemma":[0.000007418427,0.00001885902,0.00009423766,0.000009313244,0.00001320368,0.00005300192,0.00002563093,0.8465864,0.0003521401,0.1495491,0.00328133,0.000009499957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00345615,0.000263252,0.993129,0.0001758199,0.00004460857,0.00002639781,0.0001009797,0.0001332293,0.002670551],"genre_scores_gemma":[0.2012551,0.001619565,0.7789452,0.0004086174,0.0006273931,0.0003541801,0.0006776181,0.0003603942,0.01575194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005179895,"threshold_uncertainty_score":0.01732844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01887177657334158,"score_gpt":0.2737245092685753,"score_spread":0.2548527326952337,"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."}}