{"id":"W2118975867","doi":"10.1007/s13042-013-0210-4","title":"EM-type method for measuring graph dissimilarity","year":2013,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Université de Sherbrooke","keywords":"Adjacency matrix; Bipartite graph; Similarity measure; Computer science; Pairwise comparison; Distance matrix; Measure (data warehouse); Graph; Mathematics; Algorithm; Pattern recognition (psychology); Artificial intelligence; Theoretical computer science; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000729808,0.00009448732,0.0001379857,0.0001454235,0.00008289666,0.0002416266,0.0005300921,0.00003927955,0.00002370223],"category_scores_gemma":[0.0002895933,0.00007659647,0.00009516365,0.00008759429,0.00002276363,0.0002509116,0.000105844,0.0003062236,0.000002607813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001179884,"about_ca_system_score_gemma":0.00001780948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000026192,"about_ca_topic_score_gemma":0.000003199387,"domain_scores_codex":[0.9990728,0.0001155685,0.000264868,0.0001219446,0.0003007217,0.0001241281],"domain_scores_gemma":[0.998786,0.0002620691,0.0002353955,0.00006861369,0.0005567932,0.00009116506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007518184,0.0001579268,0.01780045,0.0000197016,0.0003744257,0.0000330881,0.002004607,0.003547821,0.002196165,0.05614104,0.0003777484,0.9172719],"study_design_scores_gemma":[0.002013885,0.0008751892,0.02383626,0.0001526977,0.00005659315,0.0008520082,0.0002484786,0.5998363,0.002323971,0.3485117,0.02086018,0.0004327043],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05788255,0.0006671123,0.9388431,0.001467302,0.0008069504,0.000058008,0.000001552642,0.00002354361,0.0002498568],"genre_scores_gemma":[0.8130435,0.0001283513,0.186177,0.0001712512,0.0001750023,0.000001680229,0.000001533733,0.000008004145,0.0002936863],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9168391,"threshold_uncertainty_score":0.3123513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0142204901757472,"score_gpt":0.2833475307028529,"score_spread":0.2691270405271057,"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."}}