{"id":"W4401747735","doi":"10.1109/tsp.2024.3446453","title":"Spectral Graph Learning With Core Eigenvectors Prior via Iterative GLASSO and Projection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Eigenvalues and eigenvectors; Computer science; Graph; Spectral graph theory; Projection (relational algebra); Core (optical fiber); Iterative method; Artificial intelligence; Mathematics; Algorithm; Theoretical computer science; Voltage graph; Telecommunications; Physics; Line graph","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001404536,0.0017971,0.001666737,0.001721587,0.0007455535,0.00131539,0.002073311,0.001852024,0.004823309],"category_scores_gemma":[0.005699183,0.0009615083,0.001412356,0.002011224,0.001937913,0.002355475,0.002500584,0.0028862,0.002940797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233471,"about_ca_system_score_gemma":0.003414012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01068675,"about_ca_topic_score_gemma":0.01831993,"domain_scores_codex":[0.9990144,0.0002849057,0.00003777602,0.0002900693,0.0002561196,0.0001167675],"domain_scores_gemma":[0.9983767,0.0007385134,0.000113758,0.000372863,0.0002791292,0.0001189783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002497393,0.0001637374,0.00090227,0.0002723021,0.000136074,0.0001374121,0.0002186085,0.4959421,0.007199275,0.06738061,0.01567192,0.411726],"study_design_scores_gemma":[0.00001299471,0.00001984985,0.0001100389,0.00001258299,0.000009035701,0.00003106554,0.00001789821,0.9682922,0.001399492,0.02855196,0.001529693,0.00001313574],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002910554,0.0001186458,0.9950461,0.0001619879,0.00002535332,0.00003179951,0.00007411717,0.0007853162,0.0008461828],"genre_scores_gemma":[0.1461128,0.0004831867,0.843619,0.0003956202,0.0001311947,0.0002790365,0.001366684,0.0007389943,0.006873531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01068675,"threshold_uncertainty_score":0.02124912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159157065472753,"score_gpt":0.2551145788556691,"score_spread":0.2391988723083938,"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."}}