{"id":"W2964105372","doi":"10.1109/tsp.2017.2752689","title":"On the Shift Operator, Graph Frequency, and Optimal Filtering in Graph Signal Processing","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graph energy; Adjacency matrix; Laplacian matrix; Spectral graph theory; Mathematics; Voltage graph; Line graph; Algorithm; Graph; Discrete mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007797497,0.0007819728,0.0004522023,0.0009298712,0.0003977502,0.001076969,0.0004832686,0.0009724786,0.00213178],"category_scores_gemma":[0.002478829,0.0002417877,0.0006274218,0.001210048,0.002173024,0.001956953,0.0009053624,0.001247335,0.000585143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00057267,"about_ca_system_score_gemma":0.0005336484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001502434,"about_ca_topic_score_gemma":0.001035077,"domain_scores_codex":[0.9995143,0.0001623704,0.00002298968,0.0001207291,0.0001398784,0.00003972412],"domain_scores_gemma":[0.9991329,0.0005452154,0.00008559289,0.00008795144,0.0001162656,0.00003206971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004852758,0.00003438607,0.0002531688,0.0001251055,0.00002258865,0.0001261844,0.0001781661,0.1232738,0.01227311,0.7919921,0.001858827,0.06981415],"study_design_scores_gemma":[0.000007752136,0.00007761971,0.0002748024,0.00003197373,0.00001576651,0.0001339535,0.00005000296,0.5248328,0.003330825,0.46524,0.005967724,0.00003664014],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00640945,0.0007471588,0.9893245,0.0002600382,0.00006940548,0.00001197153,0.00002614189,0.00005591511,0.003095337],"genre_scores_gemma":[0.4154693,0.006573051,0.5679138,0.0006229475,0.0008013203,0.0002080565,0.0002388606,0.0002548503,0.007917789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00213178,"threshold_uncertainty_score":0.007131577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02521948641295206,"score_gpt":0.2665720012858287,"score_spread":0.2413525148728766,"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."}}