{"id":"W4306317318","doi":"10.1145/3511808.3557688","title":"Sampling Enclosing Subgraphs for Link Prediction","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Scalability; Computer science; Overhead (engineering); Link (geometry); Graph; Theoretical computer science; Representation (politics); Induced subgraph isomorphism problem; Line graph; Computer network","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":[0.0004561679,0.001001539,0.0008633368,0.001757689,0.0005148491,0.0005755079,0.00117548,0.000948896,0.001950437],"category_scores_gemma":[0.004183988,0.0004036204,0.0007304894,0.002109259,0.0005624903,0.002168436,0.0009992217,0.001064768,0.0009311173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005804265,"about_ca_system_score_gemma":0.0006826707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007337757,"about_ca_topic_score_gemma":0.01411667,"domain_scores_codex":[0.999448,0.0001229794,0.00002008745,0.0002165451,0.0001351853,0.00005707736],"domain_scores_gemma":[0.998453,0.0007202186,0.0001866765,0.0004176861,0.0001548664,0.00006762702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002720271,0.0001870092,0.01194537,0.000225061,0.0001392481,0.000324084,0.0002059438,0.6410739,0.01127905,0.01367014,0.01657819,0.3041],"study_design_scores_gemma":[0.000008793172,0.00002417566,0.0007619733,0.000009823038,0.00001558769,0.00005441598,0.00003388061,0.9795084,0.00215751,0.01591856,0.001499849,0.000006994233],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1162003,0.0008496318,0.8706228,0.0004386414,0.00006182853,0.0001213979,0.002172193,0.00692746,0.002605833],"genre_scores_gemma":[0.7827011,0.000519782,0.2057042,0.0002461052,0.00008552866,0.0001517075,0.007692682,0.0004011228,0.002497816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007337757,"threshold_uncertainty_score":0.01459008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06510801511284907,"score_gpt":0.3057570359604422,"score_spread":0.2406490208475932,"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."}}