{"id":"W4386123456","doi":"10.14778/3611479.3611516","title":"Scaling Up Structural Clustering to Large Probabilistic Graphs Using Lyapunov Central Limit Theorem","year":2023,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Cluster analysis; Probabilistic logic; Theoretical computer science; Correlation clustering; Computer science; Mathematics; Algorithm; Artificial intelligence","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":[0.0003201036,0.0002251694,0.0002920153,0.0001498026,0.0002596536,0.00009620927,0.0005165204,0.00002819007,0.00007086706],"category_scores_gemma":[0.00002554613,0.0001672932,0.0002591287,0.0008972425,0.00004922314,0.0001141172,0.000704594,0.0001515292,0.000004489963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008906417,"about_ca_system_score_gemma":0.00002004166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00013791,"about_ca_topic_score_gemma":0.000005705366,"domain_scores_codex":[0.9983408,0.00001113454,0.0003840861,0.0003334444,0.0003305329,0.0005999929],"domain_scores_gemma":[0.9993303,0.00004043477,0.0002163655,0.0001781157,0.0001334245,0.0001013202],"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":[0.0001837591,0.0002127428,0.313284,0.0003997545,0.0008624966,0.000001066345,0.008271181,0.0196834,0.122588,0.5069573,0.003364629,0.0241916],"study_design_scores_gemma":[0.001787203,0.0001520667,0.05356905,0.001175047,0.0007023287,0.00000754171,0.005629052,0.4130374,0.1534598,0.3666938,0.00226551,0.001521262],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965221,0.00001297254,0.001071898,0.0001393198,0.0002125744,0.0006619312,0.00002058227,0.0001521508,0.001206468],"genre_scores_gemma":[0.9978161,0.000001573192,0.001770071,0.00003381035,0.00019076,0.00004682399,0.000005762328,0.00003086975,0.0001041867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3933539,"threshold_uncertainty_score":0.6822017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02037009250056965,"score_gpt":0.2736062578266925,"score_spread":0.2532361653261228,"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."}}