{"id":"W2950907416","doi":"10.48550/arxiv.1812.02356","title":"dynnode2vec: Scalable Dynamic Network Embedding","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Embedding; Computer science; Graph embedding; Scalability; Random walk; Theoretical computer science; Timestamp; Dynamic network analysis; Graph; Representation (politics); Vector space; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002946663,0.0005585699,0.0005249272,0.0002539925,0.0004136557,0.0002259994,0.003580349,0.0004937861,0.00003697221],"category_scores_gemma":[0.00002594648,0.0006634587,0.0003284196,0.001490245,0.0002583551,0.0007547816,0.005083165,0.001076597,0.0002582806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003225502,"about_ca_system_score_gemma":0.0001381292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002820558,"about_ca_topic_score_gemma":0.000055577,"domain_scores_codex":[0.9963887,0.0001828087,0.0003072187,0.001978374,0.0001522985,0.0009906288],"domain_scores_gemma":[0.9965638,0.0001679446,0.0004178237,0.002338788,0.0002174054,0.0002942573],"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":[0.00001985756,0.00003434009,0.0007055189,0.00003146186,0.00007619826,0.0002958101,0.00004116221,0.9460774,0.0000083319,0.04951889,0.00174716,0.001443845],"study_design_scores_gemma":[0.0002357545,0.00004635473,0.0002952194,0.0001632561,0.00003889607,0.000009604643,0.000007518845,0.8172581,0.00001198928,0.1802095,0.001138589,0.0005852469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05314774,0.0002307644,0.9387913,0.00008907431,0.002450306,0.0003525292,0.000007946644,0.0008158585,0.004114437],"genre_scores_gemma":[0.970898,0.0002953765,0.02571082,0.0002397099,0.0003209918,0.000001657359,0.00002093034,0.00004931113,0.002463218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9177502,"threshold_uncertainty_score":0.9995816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03899550368280359,"score_gpt":0.2011969557260333,"score_spread":0.1622014520432297,"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."}}