{"id":"W7131417874","doi":"10.1109/icdm65498.2025.00107","title":"Federated Graph Out-of-Distribution Generalization via Representation Propagation and Scattering","year":2025,"lang":"","type":"article","venue":"","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":"Generalization; Robustness (evolution); Graph; Representation (politics); Feature learning; Upper and lower bounds; Feature (linguistics); Topology (electrical circuits); Generalization error","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":[0.002581468,0.002005508,0.002125201,0.001325231,0.001149514,0.001714343,0.003906027,0.002184637,0.002485299],"category_scores_gemma":[0.01006597,0.0007664604,0.001730407,0.001701164,0.001962279,0.004889142,0.004678275,0.003249464,0.001064604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001912495,"about_ca_system_score_gemma":0.001739118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00538702,"about_ca_topic_score_gemma":0.007927007,"domain_scores_codex":[0.9976707,0.0007415377,0.00008440248,0.0007507919,0.0005351481,0.0002172551],"domain_scores_gemma":[0.9945581,0.002054701,0.0003755109,0.002310819,0.0005039874,0.0001969865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002821469,0.000321206,0.002416679,0.0001516051,0.0001448789,0.0002630935,0.0002553971,0.6612831,0.006212612,0.02111666,0.01022874,0.2973239],"study_design_scores_gemma":[0.00001305377,0.00002999572,0.00009592607,0.000004945144,0.000009199234,0.0000467658,0.00002090662,0.9800743,0.001302693,0.017911,0.0004839533,0.000007178555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02803701,0.0002390875,0.9659561,0.0004710646,0.00004543772,0.00007795947,0.0002190719,0.003819962,0.001134375],"genre_scores_gemma":[0.7167575,0.0002902502,0.2740764,0.0009195381,0.0001027423,0.0002404818,0.001810858,0.0006335175,0.005168694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00538702,"threshold_uncertainty_score":0.01387614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01853011709267681,"score_gpt":0.2835511549845922,"score_spread":0.2650210378919154,"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."}}