{"id":"W2604942799","doi":"10.1609/aaai.v31i1.10488","title":"Community Preserving Network Embedding","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":929,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Tencent","keywords":"Computer science; Embedding; Correctness; Community structure; Modularity (biology); Theoretical computer science; Exploit; Variety (cybernetics); Representation (politics); Feature (linguistics); Feature learning; Artificial intelligence; Non-negative matrix factorization; Matrix decomposition; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008844432,0.001340825,0.001053915,0.001389497,0.0005329156,0.0009623161,0.00165137,0.001352218,0.002819461],"category_scores_gemma":[0.004612093,0.0003969697,0.001093035,0.001266984,0.000838656,0.002542938,0.001853431,0.001745595,0.0008883986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009076539,"about_ca_system_score_gemma":0.001036291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002624474,"about_ca_topic_score_gemma":0.003462556,"domain_scores_codex":[0.9992087,0.0002694605,0.00002803048,0.0002227074,0.0002030847,0.00006808476],"domain_scores_gemma":[0.9987205,0.0005122299,0.0001947891,0.0002551455,0.0002456382,0.00007172168],"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":[0.00009162806,0.0000798146,0.001191939,0.0003395002,0.0001161163,0.0002218515,0.0001984026,0.5910562,0.00776476,0.1524678,0.01145202,0.23502],"study_design_scores_gemma":[0.000006178206,0.00001854034,0.0001369198,0.00001360422,0.000009356023,0.00006012067,0.00001502048,0.9559039,0.0007962246,0.04056243,0.002468651,0.000008950435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005000214,0.0002572985,0.9924322,0.0001473741,0.00003762259,0.00003946116,0.0001872402,0.0003540332,0.001544474],"genre_scores_gemma":[0.4399314,0.001349765,0.5449197,0.0003713707,0.0002284656,0.0004849031,0.001877731,0.0004008689,0.01043588],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002819461,"threshold_uncertainty_score":0.009432018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1242917882980847,"score_gpt":0.342916314103725,"score_spread":0.2186245258056403,"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."}}