{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.0009223125,0.0002626042,0.0003059997,0.00006304219,0.002222575,0.000862438,0.008760423,0.000105977,0.00002512496],"category_scores_gemma":[0.0009485787,0.0002032686,0.0001533051,0.0003457654,0.0005018287,0.001301335,0.002772944,0.0009041574,0.00003343441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003409009,"about_ca_system_score_gemma":0.0000439013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008734123,"about_ca_topic_score_gemma":0.00004273838,"domain_scores_codex":[0.9980026,0.00004852661,0.0005021336,0.0004227916,0.000473536,0.0005504522],"domain_scores_gemma":[0.9969682,0.000179929,0.000861347,0.001350219,0.0005251751,0.0001151403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003454955,0.00007893779,0.0009864944,0.00003062855,0.00001553142,6.92391e-7,0.0007696782,0.001821659,0.005473856,0.9332857,0.0006002812,0.05690203],"study_design_scores_gemma":[0.00002661243,0.0001305293,0.001725413,0.0004520589,0.000009113089,0.000004940029,0.0002023415,0.22726,0.0900929,0.6796217,0.0001874027,0.0002870076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5627143,0.0001270557,0.2638744,0.02052963,0.004588694,0.001854868,0.000008494451,0.0007573134,0.1455452],"genre_scores_gemma":[0.988738,0.00003147297,0.01048179,0.000229982,0.0001583147,0.00002013513,1.779351e-7,0.00001558315,0.000324525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4260237,"threshold_uncertainty_score":0.9990764,"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."}}