{"id":"W4403484636","doi":"10.2139/ssrn.4990872","title":"A Novel Hypergraph Neural Network Combining Multi-View Learning with Density Awareness for Semi-Supervised Node Classification","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Hypergraph; Artificial neural network; Node (physics); Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Mathematics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007803537,0.001094545,0.00170433,0.00121176,0.0005845822,0.001084246,0.003362118,0.00226869,0.00192268],"category_scores_gemma":[0.00214099,0.0007437391,0.0009775865,0.001524247,0.0007039835,0.002225241,0.002144766,0.001647327,0.0009491484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009412433,"about_ca_system_score_gemma":0.001147884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01204562,"about_ca_topic_score_gemma":0.01628524,"domain_scores_codex":[0.9993092,0.0001511619,0.00003073335,0.0002649475,0.0001665792,0.00007746919],"domain_scores_gemma":[0.999024,0.0003955152,0.00006957404,0.0001702381,0.000260845,0.00007985859],"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":[0.0002642454,0.0003160852,0.002060509,0.0001516301,0.0002725425,0.0001274474,0.0001113202,0.3486091,0.01252864,0.007178131,0.00826811,0.6201122],"study_design_scores_gemma":[0.000003804892,0.00001601479,0.0001065461,0.000003806914,0.00001084833,0.00001308505,0.00000517018,0.9974783,0.0006463082,0.001503555,0.0002080287,0.000004546309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01622628,0.0005406399,0.9802434,0.0002334406,0.0001041449,0.00006663844,0.0001964399,0.001444247,0.0009446972],"genre_scores_gemma":[0.52088,0.0006227482,0.4691739,0.000616674,0.0002672368,0.0002972326,0.001311187,0.00028859,0.006542373],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01204562,"threshold_uncertainty_score":0.02395099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03711061810186388,"score_gpt":0.2767516360345949,"score_spread":0.239641017932731,"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."}}