{"id":"W4417196708","doi":"10.1145/3780098","title":"Enhancing Interpretability of Graph Convolutional Networks for Multi-view Learning","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Interpretability; Graph; Feature learning; Convolutional neural network; Feature (linguistics); Subspace topology; Variety (cybernetics)","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":[0.0004178121,0.0001953411,0.0002859478,0.000242452,0.001079201,0.00005405711,0.001726243,0.000105853,0.00000197279],"category_scores_gemma":[0.00009334796,0.0002105696,0.0001578131,0.0010115,0.0003703344,0.0001796813,0.0001856548,0.0005079259,0.000001415069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004308026,"about_ca_system_score_gemma":0.00006359912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000188248,"about_ca_topic_score_gemma":0.00004890268,"domain_scores_codex":[0.9984223,0.0001603995,0.0005929536,0.0004629905,0.000102647,0.0002586808],"domain_scores_gemma":[0.9942632,0.003106748,0.000222267,0.002029253,0.0002969745,0.00008161415],"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":[0.00001368929,0.0005708679,0.0007330685,0.00008576077,0.0001120975,4.730253e-8,0.0003742204,0.05436524,0.0008795286,0.04136458,0.00001197145,0.9014889],"study_design_scores_gemma":[0.0005282974,0.00004886343,0.001245805,0.0001436293,0.00004028564,0.000001994428,0.00007477405,0.9898953,0.0005022237,0.004274259,0.003065107,0.0001794199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000392632,0.001306115,0.9959642,0.0009403056,0.0001096957,0.001000172,0.00001335732,0.0002247276,0.00004880908],"genre_scores_gemma":[0.5121674,0.0005037076,0.4868278,0.0001005326,0.00001057356,0.000336304,0.00001801387,0.000007867464,0.00002781913],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9355301,"threshold_uncertainty_score":0.858678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02565939714885769,"score_gpt":0.320001555200113,"score_spread":0.2943421580512554,"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."}}