{"id":"W4410637878","doi":"10.1145/3701716.3715863","title":"Graph Machine Learning under Distribution Shifts: Adaptation, Generalization and Extension to LLM","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; National Key Research and Development Program of China; Microsoft Research Asia; Weill Cornell Medical College; Tsinghua University; Beijing National Research Center For Information Science And Technology; Microsoft Research; National Natural Science Foundation of China; Universitas Brawijaya; York University; Institute for Catastrophic Loss Reduction","keywords":"Computer science; Extension (predicate logic); Generalization; Graph; Adaptation (eye); Theoretical computer science; Artificial intelligence; Machine learning; Mathematics; Programming language; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001088901,0.0000995146,0.00008717205,0.0001065419,0.0002092425,0.00009065344,0.0001512206,0.0000408862,0.000003047589],"category_scores_gemma":[0.00004270082,0.00008896733,0.00002184735,0.0009699477,0.00001872337,0.0003608112,0.0001552568,0.00009166981,0.000003349656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002492101,"about_ca_system_score_gemma":0.00001190182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002866575,"about_ca_topic_score_gemma":0.00006432272,"domain_scores_codex":[0.9991616,0.00005988301,0.0001537637,0.000346582,0.0001210524,0.0001571271],"domain_scores_gemma":[0.9995443,0.00005571261,0.00003895606,0.0002013299,0.00009866084,0.00006101199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009373267,0.00002114698,0.001091328,0.000005435264,0.000007254568,0.000001605513,0.0000982493,0.2364095,0.0009888877,0.7179253,0.001117309,0.04232468],"study_design_scores_gemma":[0.0002193661,0.00006553908,0.02486553,0.00002656615,0.0000052957,0.000002385181,0.00002678611,0.8939131,0.0003975349,0.07671054,0.003618907,0.0001484661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01005318,0.000260894,0.9854179,0.003542342,0.0001741016,0.0001354281,0.000001055727,0.0002135281,0.0002015742],"genre_scores_gemma":[0.9519342,0.00007593335,0.04510025,0.002353888,0.00001683196,0.000009641337,0.00005034834,0.000005100128,0.0004538073],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.941881,"threshold_uncertainty_score":0.3627982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289816893168876,"score_gpt":0.2492417734022997,"score_spread":0.2363436044706109,"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."}}