{"id":"W4405908075","doi":"10.1109/tpami.2024.3524180","title":"Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; York University; College of Veterinarians of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Graph; Graph theory; Algorithm; Computer vision; Combinatorics; Pattern recognition (psychology); Mathematics; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004627243,0.0002808627,0.0002929171,0.0008116354,0.0002643224,0.0005904117,0.0005267524,0.00004310343,0.0001798592],"category_scores_gemma":[0.00000196222,0.000231727,0.0003382258,0.001660384,0.0000486621,0.0002207834,0.0000191989,0.0002385146,0.0001105933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005316363,"about_ca_system_score_gemma":0.00001134102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007450662,"about_ca_topic_score_gemma":0.0004449586,"domain_scores_codex":[0.9979728,0.00007296366,0.0003812624,0.0008268748,0.000407462,0.0003386596],"domain_scores_gemma":[0.9990216,0.0001539478,0.00005462834,0.0006039403,0.00002857896,0.0001373008],"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":[0.00000390328,0.0001305966,0.00008701366,0.00004973847,0.000706123,0.00002037097,0.0005157056,0.1043348,0.0006681205,0.000237152,0.00001353712,0.8932329],"study_design_scores_gemma":[0.00005759398,0.00007594497,0.0001798032,0.00006495637,0.0005561062,0.000008303961,0.00004062223,0.9852007,0.01267541,0.00035328,0.0004200002,0.0003672546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003032743,0.0002756544,0.9951866,0.0004060899,0.0005928752,0.0001560786,0.00004519668,0.0002208373,0.00008393179],"genre_scores_gemma":[0.9935047,0.0001934564,0.005661975,0.0001837904,0.00003664622,0.0000287946,0.00001656223,0.00001471147,0.0003593806],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.990472,"threshold_uncertainty_score":0.9449552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257247706065837,"score_gpt":0.2746094131401269,"score_spread":0.2520369360794686,"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."}}